Class AbstractAmazonMachineLearning

java.lang.Object
com.amazonaws.services.machinelearning.AbstractAmazonMachineLearning
All Implemented Interfaces:
AmazonMachineLearning
Direct Known Subclasses:
AbstractAmazonMachineLearningAsync
public class AbstractAmazonMachineLearning extends Object implements AmazonMachineLearning
Abstract implementation of AmazonMachineLearning. Convenient method forms pass through to the corresponding overload that takes a request object, which throws an UnsupportedOperationException.

  • Constructor Details

    • AbstractAmazonMachineLearning

      protected AbstractAmazonMachineLearning()

  • Method Details

    • setEndpoint

      public void setEndpoint(String endpoint)
      Description copied from 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 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.

      Specified by:
      setEndpoint in interface AmazonMachineLearning
      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.

    • setRegion

      public void setRegion(Region region)
      Description copied from interface: AmazonMachineLearning
      An alternative to AmazonMachineLearning.setEndpoint(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 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.

      Specified by:
      setRegion in interface AmazonMachineLearning
      Parameters:
      region - The region this client will communicate with. See Region.getRegion(com.amazonaws.regions.Regions) for accessing a given region. Must not be null and must be a region where the service is available.
      See Also:

    • createBatchPrediction

      public CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      createBatchPrediction in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateBatchPrediction operation returned by the service.

    • createDataSourceFromRDS

      public CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      createDataSourceFromRDS in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateDataSourceFromRDS operation returned by the service.

    • createDataSourceFromRedshift

      public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      createDataSourceFromRedshift in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateDataSourceFromRedshift operation returned by the service.

    • createDataSourceFromS3

      public CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      createDataSourceFromS3 in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateDataSourceFromS3 operation returned by the service.

    • createEvaluation

      public CreateEvaluationResult createEvaluation(CreateEvaluationRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      createEvaluation in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateEvaluation operation returned by the service.

    • createMLModel

      public CreateMLModelResult createMLModel(CreateMLModelRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      createMLModel in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateMLModel operation returned by the service.

    • createRealtimeEndpoint

      public CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest request)
      Description copied from interface: AmazonMachineLearning

      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 .

      Specified by:
      createRealtimeEndpoint in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the CreateRealtimeEndpoint operation returned by the service.

    • deleteBatchPrediction

      public DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      deleteBatchPrediction in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DeleteBatchPrediction operation returned by the service.

    • deleteDataSource

      public DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      deleteDataSource in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DeleteDataSource operation returned by the service.

    • deleteEvaluation

      public DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      deleteEvaluation in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DeleteEvaluation operation returned by the service.

    • deleteMLModel

      public DeleteMLModelResult deleteMLModel(DeleteMLModelRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      deleteMLModel in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DeleteMLModel operation returned by the service.

    • deleteRealtimeEndpoint

      public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)
      Description copied from interface: AmazonMachineLearning

      Deletes a real time endpoint of an MLModel.

      Specified by:
      deleteRealtimeEndpoint in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DeleteRealtimeEndpoint operation returned by the service.

    • describeBatchPredictions

      public DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest request)
      Description copied from interface: AmazonMachineLearning

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

      Specified by:
      describeBatchPredictions in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DescribeBatchPredictions operation returned by the service.

    • describeBatchPredictions

      public DescribeBatchPredictionsResult describeBatchPredictions()
      Description copied from interface: AmazonMachineLearning
      Simplified method form for invoking the DescribeBatchPredictions operation.
      Specified by:
      describeBatchPredictions in interface AmazonMachineLearning
      See Also:

    • describeDataSources

      public DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest request)
      Description copied from interface: AmazonMachineLearning

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

      Specified by:
      describeDataSources in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DescribeDataSources operation returned by the service.

    • describeDataSources

      public DescribeDataSourcesResult describeDataSources()
      Description copied from interface: AmazonMachineLearning
      Simplified method form for invoking the DescribeDataSources operation.
      Specified by:
      describeDataSources in interface AmazonMachineLearning
      See Also:

    • describeEvaluations

      public DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest request)
      Description copied from interface: AmazonMachineLearning

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

      Specified by:
      describeEvaluations in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DescribeEvaluations operation returned by the service.

    • describeEvaluations

      public DescribeEvaluationsResult describeEvaluations()
      Description copied from interface: AmazonMachineLearning
      Simplified method form for invoking the DescribeEvaluations operation.
      Specified by:
      describeEvaluations in interface AmazonMachineLearning
      See Also:

    • describeMLModels

      public DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest request)
      Description copied from interface: AmazonMachineLearning

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

      Specified by:
      describeMLModels in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the DescribeMLModels operation returned by the service.

    • describeMLModels

      public DescribeMLModelsResult describeMLModels()
      Description copied from interface: AmazonMachineLearning
      Simplified method form for invoking the DescribeMLModels operation.
      Specified by:
      describeMLModels in interface AmazonMachineLearning
      See Also:

    • getBatchPrediction

      public GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest request)
      Description copied from interface: AmazonMachineLearning

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

      Specified by:
      getBatchPrediction in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the GetBatchPrediction operation returned by the service.

    • getDataSource

      public GetDataSourceResult getDataSource(GetDataSourceRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      getDataSource in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the GetDataSource operation returned by the service.

    • getEvaluation

      public GetEvaluationResult getEvaluation(GetEvaluationRequest request)
      Description copied from interface: AmazonMachineLearning

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

      Specified by:
      getEvaluation in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the GetEvaluation operation returned by the service.

    • getMLModel

      public GetMLModelResult getMLModel(GetMLModelRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      getMLModel in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the GetMLModel operation returned by the service.

    • predict

      public PredictResult predict(PredictRequest request)
      Description copied from interface: AmazonMachineLearning

      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.

      Specified by:
      predict in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the Predict operation returned by the service.

    • updateBatchPrediction

      public UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest request)
      Description copied from interface: AmazonMachineLearning

      Updates the BatchPredictionName of a BatchPrediction.

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

      Specified by:
      updateBatchPrediction in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the UpdateBatchPrediction operation returned by the service.

    • updateDataSource

      public UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest request)
      Description copied from interface: AmazonMachineLearning

      Updates the DataSourceName of a DataSource.

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

      Specified by:
      updateDataSource in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the UpdateDataSource operation returned by the service.

    • updateEvaluation

      public UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest request)
      Description copied from interface: AmazonMachineLearning

      Updates the EvaluationName of an Evaluation.

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

      Specified by:
      updateEvaluation in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the UpdateEvaluation operation returned by the service.

    • updateMLModel

      public UpdateMLModelResult updateMLModel(UpdateMLModelRequest request)
      Description copied from interface: AmazonMachineLearning

      Updates the MLModelName and the ScoreThreshold of an MLModel.

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

      Specified by:
      updateMLModel in interface AmazonMachineLearning
      Parameters:
      request -
      Returns:
      Result of the UpdateMLModel operation returned by the service.

    • shutdown

      public void shutdown()
      Description copied from interface: AmazonMachineLearning
      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.
      Specified by:
      shutdown in interface AmazonMachineLearning

    • getCachedResponseMetadata

      public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
      Description copied from interface: AmazonMachineLearning
      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.

      Specified by:
      getCachedResponseMetadata in interface AmazonMachineLearning
      Parameters:
      request - The originally executed request.
      Returns:
      The response metadata for the specified request, or null if none is available.