Start line:  
End line:  

Snippet Preview

Snippet HTML Code

Stack Overflow Questions
   /*
    * Copyright 2010-2015 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 com.amazonaws.*;
Interface for accessing AmazonMachineLearning.

Definition of the public APIs exposed by Amazon Machine Learning

  
  public interface AmazonMachineLearning {

    
Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com"). Callers can use this method to control which AWS region they want to work with.

Callers can pass in just the endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the protocol is not specified here, the default protocol from this client's com.amazonaws.ClientConfiguration will be used, which by default is HTTPS.

For more information on using AWS regions with the AWS SDK for Java, and a complete list of all available endpoints for all AWS services, see: http://developer.amazonwebservices.com/connect/entry.jspa?externalID=3912

This method is not threadsafe. An endpoint should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.

Parameters:
endpoint The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com") of the region specific AWS endpoint this client will communicate with.
Throws:
java.lang.IllegalArgumentException If any problems are detected with the specified endpoint.
  
      public void setEndpoint(String endpointthrows java.lang.IllegalArgumentException;

    
An alternative to setEndpoint(java.lang.String), sets the regional endpoint for this client's service calls. Callers can use this method to control which AWS region they want to work with.

By default, all service endpoints in all regions use the https protocol. To use http instead, specify it in the com.amazonaws.ClientConfiguration supplied at construction.

This method is not threadsafe. A region should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.

  
      public void setRegion(Region regionthrows java.lang.IllegalArgumentException;
    
    

Updates the EvaluationName of an Evaluation .

You can use the GetEvaluation operation to view the contents of the updated data element.

Parameters:
updateEvaluationRequest Container for the necessary parameters to execute the UpdateEvaluation service method on AmazonMachineLearning.
Returns:
The response from the UpdateEvaluation service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
createMLModelRequest Container for the necessary parameters to execute the CreateMLModel service method on AmazonMachineLearning.
Returns:
The response from the CreateMLModel service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public CreateMLModelResult createMLModel(CreateMLModelRequest createMLModelRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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 .

Parameters:
createRealtimeEndpointRequest Container for the necessary parameters to execute the CreateRealtimeEndpoint service method on AmazonMachineLearning.
Returns:
The response from the CreateRealtimeEndpoint service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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 .

Parameters:
createDataSourceFromS3Request Container for the necessary parameters to execute the CreateDataSourceFromS3 service method on AmazonMachineLearning.
Returns:
The response from the CreateDataSourceFromS3 service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
deleteMLModelRequest Container for the necessary parameters to execute the DeleteMLModel service method on AmazonMachineLearning.
Returns:
The response from the DeleteMLModel service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DeleteMLModelResult deleteMLModel(DeleteMLModelRequest deleteMLModelRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Generates a prediction for the observation using the specified MLModel .

NOTE: Note Not all response parameters will be populated because this is dependent on the type of requested model.

Parameters:
predictRequest Container for the necessary parameters to execute the Predict service method on AmazonMachineLearning.
Returns:
The response from the Predict service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.PredictorNotMountedException
com.amazonaws.services.machinelearning.model.LimitExceededException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public PredictResult predict(PredictRequest predictRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Returns a list of BatchPrediction operations that match the search criteria in the request.

Parameters:
describeBatchPredictionsRequest Container for the necessary parameters to execute the DescribeBatchPredictions service method on AmazonMachineLearning.
Returns:
The response from the DescribeBatchPredictions service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
             throws AmazonServiceExceptionAmazonClientException;

    

Returns an Evaluation that includes metadata as well as the current status of the Evaluation .

Parameters:
getEvaluationRequest Container for the necessary parameters to execute the GetEvaluation service method on AmazonMachineLearning.
Returns:
The response from the GetEvaluation service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public GetEvaluationResult getEvaluation(GetEvaluationRequest getEvaluationRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Updates the MLModelName and the ScoreThreshold of an MLModel .

You can use the GetMLModel operation to view the contents of the updated data element.

Parameters:
updateMLModelRequest Container for the necessary parameters to execute the UpdateMLModel service method on AmazonMachineLearning.
Returns:
The response from the UpdateMLModel service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public UpdateMLModelResult updateMLModel(UpdateMLModelRequest updateMLModelRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
getDataSourceRequest Container for the necessary parameters to execute the GetDataSource service method on AmazonMachineLearning.
Returns:
The response from the GetDataSource service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public GetDataSourceResult getDataSource(GetDataSourceRequest getDataSourceRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Returns a list of DataSource that match the search criteria in the request.

Parameters:
describeDataSourcesRequest Container for the necessary parameters to execute the DescribeDataSources service method on AmazonMachineLearning.
Returns:
The response from the DescribeDataSources service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
deleteEvaluationRequest Container for the necessary parameters to execute the DeleteEvaluation service method on AmazonMachineLearning.
Returns:
The response from the DeleteEvaluation service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Updates the BatchPredictionName of a BatchPrediction .

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Parameters:
updateBatchPredictionRequest Container for the necessary parameters to execute the UpdateBatchPrediction service method on AmazonMachineLearning.
Returns:
The response from the UpdateBatchPrediction service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
createBatchPredictionRequest Container for the necessary parameters to execute the CreateBatchPrediction service method on AmazonMachineLearning.
Returns:
The response from the CreateBatchPrediction service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Returns a list of MLModel that match the search criteria in the request.

Parameters:
describeMLModelsRequest Container for the necessary parameters to execute the DescribeMLModels service method on AmazonMachineLearning.
Returns:
The response from the DescribeMLModels service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest describeMLModelsRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
deleteBatchPredictionRequest Container for the necessary parameters to execute the DeleteBatchPrediction service method on AmazonMachineLearning.
Returns:
The response from the DeleteBatchPrediction service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Updates the DataSourceName of a DataSource .

You can use the GetDataSource operation to view the contents of the updated data element.

Parameters:
updateDataSourceRequest Container for the necessary parameters to execute the UpdateDataSource service method on AmazonMachineLearning.
Returns:
The response from the UpdateDataSource service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest updateDataSourceRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
createDataSourceFromRDSRequest Container for the necessary parameters to execute the CreateDataSourceFromRDS service method on AmazonMachineLearning.
Returns:
The response from the CreateDataSourceFromRDS service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
createDataSourceFromRedshiftRequest Container for the necessary parameters to execute the CreateDataSourceFromRedshift service method on AmazonMachineLearning.
Returns:
The response from the CreateDataSourceFromRedshift service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
             throws AmazonServiceExceptionAmazonClientException;

    

Returns a list of DescribeEvaluations that match the search criteria in the request.

Parameters:
describeEvaluationsRequest Container for the necessary parameters to execute the DescribeEvaluations service method on AmazonMachineLearning.
Returns:
The response from the DescribeEvaluations service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
getMLModelRequest Container for the necessary parameters to execute the GetMLModel service method on AmazonMachineLearning.
Returns:
The response from the GetMLModel service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public GetMLModelResult getMLModel(GetMLModelRequest getMLModelRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
deleteDataSourceRequest Container for the necessary parameters to execute the DeleteDataSource service method on AmazonMachineLearning.
Returns:
The response from the DeleteDataSource service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Parameters:
getBatchPredictionRequest Container for the necessary parameters to execute the GetBatchPrediction service method on AmazonMachineLearning.
Returns:
The response from the GetBatchPrediction service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest
             throws AmazonServiceExceptionAmazonClientException;

    

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.

Parameters:
createEvaluationRequest Container for the necessary parameters to execute the CreateEvaluation service method on AmazonMachineLearning.
Returns:
The response from the CreateEvaluation service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public CreateEvaluationResult createEvaluation(CreateEvaluationRequest createEvaluationRequest
             throws AmazonServiceExceptionAmazonClientException;

    

Deletes a real time endpoint of an MLModel .

Parameters:
deleteRealtimeEndpointRequest Container for the necessary parameters to execute the DeleteRealtimeEndpoint service method on AmazonMachineLearning.
Returns:
The response from the DeleteRealtimeEndpoint service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.ResourceNotFoundException
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
 
     public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest
            throws AmazonServiceExceptionAmazonClientException;

    

Returns a list of BatchPrediction operations that match the search criteria in the request.

Returns:
The response from the DescribeBatchPredictions service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.

Returns a list of DataSource that match the search criteria in the request.

Returns:
The response from the DescribeDataSources service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.

Returns a list of MLModel that match the search criteria in the request.

Returns:
The response from the DescribeMLModels service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.

Returns a list of DescribeEvaluations that match the search criteria in the request.

Returns:
The response from the DescribeEvaluations service method, as returned by AmazonMachineLearning.
Throws:
com.amazonaws.services.machinelearning.model.InvalidInputException
com.amazonaws.services.machinelearning.model.InternalServerException
com.amazonaws.AmazonClientException If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available.
com.amazonaws.AmazonServiceException If an error response is returned by AmazonMachineLearning indicating either a problem with the data in the request, or a server side issue.
Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.
    public void shutdown();
    
    
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 a request.

Parameters:
request The originally executed request.
Returns:
The response metadata for the specified request, or null if none is available.
}
        
New to GrepCode? Check out our FAQ X