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/*
* Copyright 2010-2016 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 com.amazonaws.services.machinelearning;
import org.w3c.dom.*;
import java.net.*;
import java.util.*;
import java.util.Map.Entry;
import org.apache.commons.logging.*;
import com.amazonaws.*;
import com.amazonaws.auth.*;
import com.amazonaws.handlers.*;
import com.amazonaws.http.*;
import com.amazonaws.internal.*;
import com.amazonaws.metrics.*;
import com.amazonaws.regions.*;
import com.amazonaws.transform.*;
import com.amazonaws.util.*;
import com.amazonaws.protocol.json.*;
import com.amazonaws.util.AWSRequestMetrics.Field;
import com.amazonaws.annotation.ThreadSafe;
import com.amazonaws.services.machinelearning.model.*;
import com.amazonaws.services.machinelearning.model.transform.*;
/**
* Client for accessing Amazon Machine Learning. All service calls made using
* this client are blocking, and will not return until the service call
* completes.
*
* Definition of the public APIs exposed by Amazon Machine Learning
*/
@ThreadSafe
public class AmazonMachineLearningClient extends AmazonWebServiceClient
implements AmazonMachineLearning {
/** Provider for AWS credentials. */
private AWSCredentialsProvider awsCredentialsProvider;
private static final Log log = LogFactory
.getLog(AmazonMachineLearning.class);
/** Default signing name for the service. */
private static final String DEFAULT_SIGNING_NAME = "machinelearning";
/** The region metadata service name for computing region endpoints. */
private static final String DEFAULT_ENDPOINT_PREFIX = "machinelearning";
/**
* Client configuration factory providing ClientConfigurations tailored to
* this client
*/
protected static final ClientConfigurationFactory configFactory = new ClientConfigurationFactory();
private final SdkJsonProtocolFactory protocolFactory = new SdkJsonProtocolFactory(
new JsonClientMetadata()
.withProtocolVersion("1.1")
.withSupportsCbor(false)
.addErrorMetadata(
new JsonErrorShapeMetadata()
.withErrorCode("InternalServerException")
.withModeledClass(
com.amazonaws.services.machinelearning.model.InternalServerException.class))
.addErrorMetadata(
new JsonErrorShapeMetadata()
.withErrorCode("LimitExceededException")
.withModeledClass(
com.amazonaws.services.machinelearning.model.LimitExceededException.class))
.addErrorMetadata(
new JsonErrorShapeMetadata()
.withErrorCode("InvalidInputException")
.withModeledClass(
com.amazonaws.services.machinelearning.model.InvalidInputException.class))
.addErrorMetadata(
new JsonErrorShapeMetadata()
.withErrorCode(
"IdempotentParameterMismatchException")
.withModeledClass(
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException.class))
.addErrorMetadata(
new JsonErrorShapeMetadata()
.withErrorCode(
"PredictorNotMountedException")
.withModeledClass(
com.amazonaws.services.machinelearning.model.PredictorNotMountedException.class))
.addErrorMetadata(
new JsonErrorShapeMetadata()
.withErrorCode("ResourceNotFoundException")
.withModeledClass(
com.amazonaws.services.machinelearning.model.ResourceNotFoundException.class)));
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning. A credentials provider chain will be used that searches for
* credentials in this order:
*
* - Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY
* - Java System Properties - aws.accessKeyId and aws.secretKey
* - Instance profile credentials delivered through the Amazon EC2
* metadata service
*
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @see DefaultAWSCredentialsProviderChain
*/
public AmazonMachineLearningClient() {
this(new DefaultAWSCredentialsProviderChain(), configFactory
.getConfig());
}
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning. A credentials provider chain will be used that searches for
* credentials in this order:
*
* - Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY
* - Java System Properties - aws.accessKeyId and aws.secretKey
* - Instance profile credentials delivered through the Amazon EC2
* metadata service
*
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @param clientConfiguration
* The client configuration options controlling how this client
* connects to Amazon Machine Learning (ex: proxy settings, retry
* counts, etc.).
*
* @see DefaultAWSCredentialsProviderChain
*/
public AmazonMachineLearningClient(ClientConfiguration clientConfiguration) {
this(new DefaultAWSCredentialsProviderChain(), clientConfiguration);
}
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning using the specified AWS account credentials.
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @param awsCredentials
* The AWS credentials (access key ID and secret key) to use when
* authenticating with AWS services.
*/
public AmazonMachineLearningClient(AWSCredentials awsCredentials) {
this(awsCredentials, configFactory.getConfig());
}
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning using the specified AWS account credentials and client
* configuration options.
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @param awsCredentials
* The AWS credentials (access key ID and secret key) to use when
* authenticating with AWS services.
* @param clientConfiguration
* The client configuration options controlling how this client
* connects to Amazon Machine Learning (ex: proxy settings, retry
* counts, etc.).
*/
public AmazonMachineLearningClient(AWSCredentials awsCredentials,
ClientConfiguration clientConfiguration) {
super(clientConfiguration);
this.awsCredentialsProvider = new StaticCredentialsProvider(
awsCredentials);
init();
}
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning using the specified AWS account credentials provider.
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @param awsCredentialsProvider
* The AWS credentials provider which will provide credentials to
* authenticate requests with AWS services.
*/
public AmazonMachineLearningClient(
AWSCredentialsProvider awsCredentialsProvider) {
this(awsCredentialsProvider, configFactory.getConfig());
}
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning using the specified AWS account credentials provider and client
* configuration options.
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @param awsCredentialsProvider
* The AWS credentials provider which will provide credentials to
* authenticate requests with AWS services.
* @param clientConfiguration
* The client configuration options controlling how this client
* connects to Amazon Machine Learning (ex: proxy settings, retry
* counts, etc.).
*/
public AmazonMachineLearningClient(
AWSCredentialsProvider awsCredentialsProvider,
ClientConfiguration clientConfiguration) {
this(awsCredentialsProvider, clientConfiguration, null);
}
/**
* Constructs a new client to invoke service methods on Amazon Machine
* Learning using the specified AWS account credentials provider, client
* configuration options, and request metric collector.
*
*
* All service calls made using this new client object are blocking, and
* will not return until the service call completes.
*
* @param awsCredentialsProvider
* The AWS credentials provider which will provide credentials to
* authenticate requests with AWS services.
* @param clientConfiguration
* The client configuration options controlling how this client
* connects to Amazon Machine Learning (ex: proxy settings, retry
* counts, etc.).
* @param requestMetricCollector
* optional request metric collector
*/
public AmazonMachineLearningClient(
AWSCredentialsProvider awsCredentialsProvider,
ClientConfiguration clientConfiguration,
RequestMetricCollector requestMetricCollector) {
super(clientConfiguration, requestMetricCollector);
this.awsCredentialsProvider = awsCredentialsProvider;
init();
}
private void init() {
setServiceNameIntern(DEFAULT_SIGNING_NAME);
setEndpointPrefix(DEFAULT_ENDPOINT_PREFIX);
// calling this.setEndPoint(...) will also modify the signer accordingly
setEndpoint("https://machinelearning.us-east-1.amazonaws.com");
HandlerChainFactory chainFactory = new HandlerChainFactory();
requestHandler2s
.addAll(chainFactory
.newRequestHandlerChain("/com/amazonaws/services/machinelearning/request.handlers"));
requestHandler2s
.addAll(chainFactory
.newRequestHandler2Chain("/com/amazonaws/services/machinelearning/request.handler2s"));
}
/**
*
* 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.
* @sample AmazonMachineLearning.CreateBatchPrediction
*/
@Override
public CreateBatchPredictionResult createBatchPrediction(
CreateBatchPredictionRequest createBatchPredictionRequest) {
ExecutionContext executionContext = createExecutionContext(createBatchPredictionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateBatchPredictionRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(createBatchPredictionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateBatchPredictionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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
* COMPLETED or PENDING status can only be used 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.
* @sample AmazonMachineLearning.CreateDataSourceFromRDS
*/
@Override
public CreateDataSourceFromRDSResult createDataSourceFromRDS(
CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest) {
ExecutionContext executionContext = createExecutionContext(createDataSourceFromRDSRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateDataSourceFromRDSRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(createDataSourceFromRDSRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateDataSourceFromRDSResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a DataSource from Amazon Redshift. 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
* status can only be used 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.
*
*
* The observations should exist in the database hosted on an Amazon
* Redshift cluster and should be specified by a SelectSqlQuery
* . Amazon ML executes
* Unload command in Amazon Redshift to transfer the result set of
* SelectSqlQuery to S3StagingLocation.
*
*
* After the DataSource is created, it's ready for use in
* evaluations and batch predictions. If you plan to use the
* DataSource to train an MLModel, the
* DataSource requires another item -- a recipe. A recipe
* describes the observation variables that participate in training an
* MLModel. A recipe describes how each input variable will be
* used in training. Will the variable be included or excluded from
* training? Will the variable be manipulated, for example, combined with
* another variable or split apart into word combinations? The recipe
* provides answers to these questions. For more information, see the Amazon
* Machine Learning Developer Guide.
*
*
* @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.
* @sample AmazonMachineLearning.CreateDataSourceFromRedshift
*/
@Override
public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(
CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest) {
ExecutionContext executionContext = createExecutionContext(createDataSourceFromRedshiftRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateDataSourceFromRedshiftRequestMarshaller(
protocolFactory)
.marshall(super
.beforeMarshalling(createDataSourceFromRedshiftRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateDataSourceFromRedshiftResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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 is
* created and ready for use, Amazon ML sets the Status
* parameter to COMPLETED. DataSource in
* COMPLETED or PENDING status can only be used 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.
*
*
* 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)
* bucket, 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 requires another item: a recipe. A recipe
* describes the observation variables that participate in training an
* MLModel. A recipe describes how each input variable will be
* used in training. Will the variable be included or excluded from
* training? Will the variable be manipulated, for example, combined with
* another variable, or split apart into word combinations? The recipe
* provides answers to these questions. For more information, see the Amazon
* Machine Learning Developer Guide.
*
*
* @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.
* @sample AmazonMachineLearning.CreateDataSourceFromS3
*/
@Override
public CreateDataSourceFromS3Result createDataSourceFromS3(
CreateDataSourceFromS3Request createDataSourceFromS3Request) {
ExecutionContext executionContext = createExecutionContext(createDataSourceFromS3Request);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateDataSourceFromS3RequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(createDataSourceFromS3Request));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateDataSourceFromS3ResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.CreateEvaluation
*/
@Override
public CreateEvaluationResult createEvaluation(
CreateEvaluationRequest createEvaluationRequest) {
ExecutionContext executionContext = createExecutionContext(createEvaluationRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateEvaluationRequestMarshaller(protocolFactory)
.marshall(super
.beforeMarshalling(createEvaluationRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateEvaluationResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Creates a new MLModel using the data files and the recipe as
* information sources.
*
*
* An MLModel is nearly immutable. Users can only update 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 is created and ready
* for use, Amazon ML sets the status to COMPLETED.
*
*
* You can use the GetMLModel operation to check 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.
* @sample AmazonMachineLearning.CreateMLModel
*/
@Override
public CreateMLModelResult createMLModel(
CreateMLModelRequest createMLModelRequest) {
ExecutionContext executionContext = createExecutionContext(createMLModelRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateMLModelRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(createMLModelRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateMLModelResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.CreateRealtimeEndpoint
*/
@Override
public CreateRealtimeEndpointResult createRealtimeEndpoint(
CreateRealtimeEndpointRequest createRealtimeEndpointRequest) {
ExecutionContext executionContext = createExecutionContext(createRealtimeEndpointRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new CreateRealtimeEndpointRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(createRealtimeEndpointRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new CreateRealtimeEndpointResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.DeleteBatchPrediction
*/
@Override
public DeleteBatchPredictionResult deleteBatchPrediction(
DeleteBatchPredictionRequest deleteBatchPredictionRequest) {
ExecutionContext executionContext = createExecutionContext(deleteBatchPredictionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteBatchPredictionRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(deleteBatchPredictionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DeleteBatchPredictionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.DeleteDataSource
*/
@Override
public DeleteDataSourceResult deleteDataSource(
DeleteDataSourceRequest deleteDataSourceRequest) {
ExecutionContext executionContext = createExecutionContext(deleteDataSourceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteDataSourceRequestMarshaller(protocolFactory)
.marshall(super
.beforeMarshalling(deleteDataSourceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DeleteDataSourceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
*
*
* @param deleteEvaluationRequest
* @return Result of the DeleteEvaluation 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.
* @sample AmazonMachineLearning.DeleteEvaluation
*/
@Override
public DeleteEvaluationResult deleteEvaluation(
DeleteEvaluationRequest deleteEvaluationRequest) {
ExecutionContext executionContext = createExecutionContext(deleteEvaluationRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteEvaluationRequestMarshaller(protocolFactory)
.marshall(super
.beforeMarshalling(deleteEvaluationRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DeleteEvaluationResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.DeleteMLModel
*/
@Override
public DeleteMLModelResult deleteMLModel(
DeleteMLModelRequest deleteMLModelRequest) {
ExecutionContext executionContext = createExecutionContext(deleteMLModelRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteMLModelRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(deleteMLModelRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DeleteMLModelResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.DeleteRealtimeEndpoint
*/
@Override
public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(
DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest) {
ExecutionContext executionContext = createExecutionContext(deleteRealtimeEndpointRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DeleteRealtimeEndpointRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(deleteRealtimeEndpointRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DeleteRealtimeEndpointResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.DescribeBatchPredictions
*/
@Override
public DescribeBatchPredictionsResult describeBatchPredictions(
DescribeBatchPredictionsRequest describeBatchPredictionsRequest) {
ExecutionContext executionContext = createExecutionContext(describeBatchPredictionsRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DescribeBatchPredictionsRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(describeBatchPredictionsRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(
new JsonOperationMetadata().withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DescribeBatchPredictionsResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
@Override
public DescribeBatchPredictionsResult describeBatchPredictions() {
return describeBatchPredictions(new 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.
* @sample AmazonMachineLearning.DescribeDataSources
*/
@Override
public DescribeDataSourcesResult describeDataSources(
DescribeDataSourcesRequest describeDataSourcesRequest) {
ExecutionContext executionContext = createExecutionContext(describeDataSourcesRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DescribeDataSourcesRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(describeDataSourcesRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DescribeDataSourcesResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
@Override
public DescribeDataSourcesResult describeDataSources() {
return describeDataSources(new 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.
* @sample AmazonMachineLearning.DescribeEvaluations
*/
@Override
public DescribeEvaluationsResult describeEvaluations(
DescribeEvaluationsRequest describeEvaluationsRequest) {
ExecutionContext executionContext = createExecutionContext(describeEvaluationsRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DescribeEvaluationsRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(describeEvaluationsRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DescribeEvaluationsResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
@Override
public DescribeEvaluationsResult describeEvaluations() {
return describeEvaluations(new 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.
* @sample AmazonMachineLearning.DescribeMLModels
*/
@Override
public DescribeMLModelsResult describeMLModels(
DescribeMLModelsRequest describeMLModelsRequest) {
ExecutionContext executionContext = createExecutionContext(describeMLModelsRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new DescribeMLModelsRequestMarshaller(protocolFactory)
.marshall(super
.beforeMarshalling(describeMLModelsRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new DescribeMLModelsResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
@Override
public DescribeMLModelsResult describeMLModels() {
return describeMLModels(new DescribeMLModelsRequest());
}
/**
*
* 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.
* @sample AmazonMachineLearning.GetBatchPrediction
*/
@Override
public GetBatchPredictionResult getBatchPrediction(
GetBatchPredictionRequest getBatchPredictionRequest) {
ExecutionContext executionContext = createExecutionContext(getBatchPredictionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new GetBatchPredictionRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(getBatchPredictionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new GetBatchPredictionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.GetDataSource
*/
@Override
public GetDataSourceResult getDataSource(
GetDataSourceRequest getDataSourceRequest) {
ExecutionContext executionContext = createExecutionContext(getDataSourceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new GetDataSourceRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(getDataSourceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new GetDataSourceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.GetEvaluation
*/
@Override
public GetEvaluationResult getEvaluation(
GetEvaluationRequest getEvaluationRequest) {
ExecutionContext executionContext = createExecutionContext(getEvaluationRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new GetEvaluationRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(getEvaluationRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new GetEvaluationResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* Returns an MLModel that includes detailed metadata, and data
* source information as well as 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.
* @sample AmazonMachineLearning.GetMLModel
*/
@Override
public GetMLModelResult getMLModel(GetMLModelRequest getMLModelRequest) {
ExecutionContext executionContext = createExecutionContext(getMLModelRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new GetMLModelRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(getMLModelRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new GetMLModelResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.Predict
*/
@Override
public PredictResult predict(PredictRequest predictRequest) {
ExecutionContext executionContext = createExecutionContext(predictRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new PredictRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(predictRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new PredictResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.UpdateBatchPrediction
*/
@Override
public UpdateBatchPredictionResult updateBatchPrediction(
UpdateBatchPredictionRequest updateBatchPredictionRequest) {
ExecutionContext executionContext = createExecutionContext(updateBatchPredictionRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new UpdateBatchPredictionRequestMarshaller(
protocolFactory).marshall(super
.beforeMarshalling(updateBatchPredictionRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new UpdateBatchPredictionResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.UpdateDataSource
*/
@Override
public UpdateDataSourceResult updateDataSource(
UpdateDataSourceRequest updateDataSourceRequest) {
ExecutionContext executionContext = createExecutionContext(updateDataSourceRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new UpdateDataSourceRequestMarshaller(protocolFactory)
.marshall(super
.beforeMarshalling(updateDataSourceRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new UpdateDataSourceResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.UpdateEvaluation
*/
@Override
public UpdateEvaluationResult updateEvaluation(
UpdateEvaluationRequest updateEvaluationRequest) {
ExecutionContext executionContext = createExecutionContext(updateEvaluationRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new UpdateEvaluationRequestMarshaller(protocolFactory)
.marshall(super
.beforeMarshalling(updateEvaluationRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new UpdateEvaluationResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
*
* 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.
* @sample AmazonMachineLearning.UpdateMLModel
*/
@Override
public UpdateMLModelResult updateMLModel(
UpdateMLModelRequest updateMLModelRequest) {
ExecutionContext executionContext = createExecutionContext(updateMLModelRequest);
AWSRequestMetrics awsRequestMetrics = executionContext
.getAwsRequestMetrics();
awsRequestMetrics.startEvent(Field.ClientExecuteTime);
Request request = null;
Response response = null;
try {
awsRequestMetrics.startEvent(Field.RequestMarshallTime);
try {
request = new UpdateMLModelRequestMarshaller(protocolFactory)
.marshall(super.beforeMarshalling(updateMLModelRequest));
// Binds the request metrics to the current request.
request.setAWSRequestMetrics(awsRequestMetrics);
} finally {
awsRequestMetrics.endEvent(Field.RequestMarshallTime);
}
HttpResponseHandler> responseHandler = protocolFactory
.createResponseHandler(new JsonOperationMetadata()
.withPayloadJson(true)
.withHasStreamingSuccessResponse(false),
new UpdateMLModelResultJsonUnmarshaller());
response = invoke(request, responseHandler, executionContext);
return response.getAwsResponse();
} finally {
endClientExecution(awsRequestMetrics, request, response);
}
}
/**
* Returns additional metadata for a previously executed successful,
* request, typically used for debugging issues where a service isn't acting
* as expected. This data isn't considered part of the result data returned
* by an operation, so it's available through this separate, diagnostic
* interface.
*
* Response metadata is only cached for a limited period of time, so if you
* need to access this extra diagnostic information for an executed request,
* you should use this method to retrieve it as soon as possible after
* executing the request.
*
* @param request
* The originally executed request
*
* @return The response metadata for the specified request, or null if none
* is available.
*/
public ResponseMetadata getCachedResponseMetadata(
AmazonWebServiceRequest request) {
return client.getResponseMetadataForRequest(request);
}
/**
* Normal invoke with authentication. Credentials are required and may be
* overriden at the request level.
**/
private Response invoke(
Request request,
HttpResponseHandler> responseHandler,
ExecutionContext executionContext) {
executionContext.setCredentialsProvider(CredentialUtils
.getCredentialsProvider(request.getOriginalRequest(),
awsCredentialsProvider));
return doInvoke(request, responseHandler, executionContext);
}
/**
* Invoke with no authentication. Credentials are not required and any
* credentials set on the client or request will be ignored for this
* operation.
**/
private Response anonymousInvoke(
Request request,
HttpResponseHandler> responseHandler,
ExecutionContext executionContext) {
return doInvoke(request, responseHandler, executionContext);
}
/**
* Invoke the request using the http client. Assumes credentials (or lack
* thereof) have been configured in the ExecutionContext beforehand.
**/
private Response doInvoke(
Request request,
HttpResponseHandler> responseHandler,
ExecutionContext executionContext) {
request.setEndpoint(endpoint);
request.setTimeOffset(timeOffset);
HttpResponseHandler errorResponseHandler = protocolFactory
.createErrorResponseHandler(new JsonErrorResponseMetadata());
return client.execute(request, responseHandler, errorResponseHandler,
executionContext);
}
}