public class AbstractAmazonMachineLearning extends Object implements AmazonMachineLearning
AmazonMachineLearning. Convenient method
forms pass through to the corresponding overload that takes a request object,
which throws an UnsupportedOperationException.| Modifier and Type | Method and Description |
|---|---|
CreateBatchPredictionResult |
createBatchPrediction(CreateBatchPredictionRequest request)
Generates predictions for a group of observations.
|
CreateDataSourceFromRDSResult |
createDataSourceFromRDS(CreateDataSourceFromRDSRequest request)
Creates a
DataSource object from an Amazon Relational Database Service
(Amazon RDS). |
CreateDataSourceFromRedshiftResult |
createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)
Creates a
DataSource from Amazon Redshift. |
CreateDataSourceFromS3Result |
createDataSourceFromS3(CreateDataSourceFromS3Request request)
Creates a
DataSource object. |
CreateEvaluationResult |
createEvaluation(CreateEvaluationRequest request)
Creates a new
Evaluation of an MLModel. |
CreateMLModelResult |
createMLModel(CreateMLModelRequest request)
Creates a new
MLModel using the data files and the recipe as
information sources. |
CreateRealtimeEndpointResult |
createRealtimeEndpoint(CreateRealtimeEndpointRequest request)
Creates a real-time endpoint for the
MLModel. |
DeleteBatchPredictionResult |
deleteBatchPrediction(DeleteBatchPredictionRequest request)
Assigns the DELETED status to a
BatchPrediction, rendering
it unusable. |
DeleteDataSourceResult |
deleteDataSource(DeleteDataSourceRequest request)
Assigns the DELETED status to a
DataSource, rendering it
unusable. |
DeleteEvaluationResult |
deleteEvaluation(DeleteEvaluationRequest request)
Assigns the
DELETED status to an Evaluation,
rendering it unusable. |
DeleteMLModelResult |
deleteMLModel(DeleteMLModelRequest request)
Assigns the DELETED status to an
MLModel, rendering it
unusable. |
DeleteRealtimeEndpointResult |
deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)
Deletes a real time endpoint of an
MLModel. |
DescribeBatchPredictionsResult |
describeBatchPredictions()
Simplified method form for invoking the DescribeBatchPredictions
operation.
|
DescribeBatchPredictionsResult |
describeBatchPredictions(DescribeBatchPredictionsRequest request)
Returns a list of
BatchPrediction operations that match the
search criteria in the request. |
DescribeDataSourcesResult |
describeDataSources()
Simplified method form for invoking the DescribeDataSources operation.
|
DescribeDataSourcesResult |
describeDataSources(DescribeDataSourcesRequest request)
Returns a list of
DataSource that match the search criteria
in the request. |
DescribeEvaluationsResult |
describeEvaluations()
Simplified method form for invoking the DescribeEvaluations operation.
|
DescribeEvaluationsResult |
describeEvaluations(DescribeEvaluationsRequest request)
Returns a list of
DescribeEvaluations that match the search
criteria in the request. |
DescribeMLModelsResult |
describeMLModels()
Simplified method form for invoking the DescribeMLModels operation.
|
DescribeMLModelsResult |
describeMLModels(DescribeMLModelsRequest request)
Returns a list of
MLModel that match the search criteria in
the request. |
GetBatchPredictionResult |
getBatchPrediction(GetBatchPredictionRequest request)
Returns a
BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request. |
ResponseMetadata |
getCachedResponseMetadata(AmazonWebServiceRequest request)
Returns additional metadata for a previously executed successful request,
typically used for debugging issues where a service isn't acting as
expected.
|
GetDataSourceResult |
getDataSource(GetDataSourceRequest request)
Returns a
DataSource that includes metadata and data file
information, as well as the current status of the DataSource
. |
GetEvaluationResult |
getEvaluation(GetEvaluationRequest request)
Returns an
Evaluation that includes metadata as well as the
current status of the Evaluation. |
GetMLModelResult |
getMLModel(GetMLModelRequest request)
Returns an
MLModel that includes detailed metadata, and data
source information as well as the current status of the
MLModel. |
PredictResult |
predict(PredictRequest request)
Generates a prediction for the observation using the specified
ML Model. |
void |
setEndpoint(String endpoint)
Overrides the default endpoint for this client
("https://machinelearning.us-east-1.amazonaws.com").
|
void |
setRegion(Region region)
An alternative to
AmazonMachineLearning.setEndpoint(String), sets
the regional endpoint for this client's service calls. |
void |
shutdown()
Shuts down this client object, releasing any resources that might be held
open.
|
UpdateBatchPredictionResult |
updateBatchPrediction(UpdateBatchPredictionRequest request)
Updates the
BatchPredictionName of a
BatchPrediction. |
UpdateDataSourceResult |
updateDataSource(UpdateDataSourceRequest request)
Updates the
DataSourceName of a DataSource. |
UpdateEvaluationResult |
updateEvaluation(UpdateEvaluationRequest request)
Updates the
EvaluationName of an Evaluation. |
UpdateMLModelResult |
updateMLModel(UpdateMLModelRequest request)
Updates the
MLModelName and the ScoreThreshold
of an MLModel. |
public void setEndpoint(String endpoint)
AmazonMachineLearning
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.
setEndpoint in interface AmazonMachineLearningendpoint - 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.public void setRegion(Region region)
AmazonMachineLearningAmazonMachineLearning.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.
setRegion in interface AmazonMachineLearningregion - 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.Region.getRegion(com.amazonaws.regions.Regions),
Region.createClient(Class,
com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration),
Region.isServiceSupported(String)public CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest request)
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.
createBatchPrediction in interface AmazonMachineLearningpublic CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest request)
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.
createDataSourceFromRDS in interface AmazonMachineLearningpublic CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)
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.
createDataSourceFromRedshift in interface AmazonMachineLearningpublic CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request request)
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.
createDataSourceFromS3 in interface AmazonMachineLearningpublic CreateEvaluationResult createEvaluation(CreateEvaluationRequest request)
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.
createEvaluation in interface AmazonMachineLearningpublic CreateMLModelResult createMLModel(CreateMLModelRequest request)
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.
createMLModel in interface AmazonMachineLearningpublic CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest request)
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
.
createRealtimeEndpoint in interface AmazonMachineLearningpublic DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest request)
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.
deleteBatchPrediction in interface AmazonMachineLearningpublic DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest request)
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.
deleteDataSource in interface AmazonMachineLearningpublic DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest request)
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.
deleteEvaluation in interface AmazonMachineLearningpublic DeleteMLModelResult deleteMLModel(DeleteMLModelRequest request)
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.
deleteMLModel in interface AmazonMachineLearningpublic DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)
AmazonMachineLearning
Deletes a real time endpoint of an MLModel.
deleteRealtimeEndpoint in interface AmazonMachineLearningpublic DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest request)
AmazonMachineLearning
Returns a list of BatchPrediction operations that match the
search criteria in the request.
describeBatchPredictions in interface AmazonMachineLearningpublic DescribeBatchPredictionsResult describeBatchPredictions()
AmazonMachineLearningdescribeBatchPredictions in interface AmazonMachineLearningAmazonMachineLearning.describeBatchPredictions(DescribeBatchPredictionsRequest)public DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest request)
AmazonMachineLearning
Returns a list of DataSource that match the search criteria
in the request.
describeDataSources in interface AmazonMachineLearningpublic DescribeDataSourcesResult describeDataSources()
AmazonMachineLearningdescribeDataSources in interface AmazonMachineLearningAmazonMachineLearning.describeDataSources(DescribeDataSourcesRequest)public DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest request)
AmazonMachineLearning
Returns a list of DescribeEvaluations that match the search
criteria in the request.
describeEvaluations in interface AmazonMachineLearningpublic DescribeEvaluationsResult describeEvaluations()
AmazonMachineLearningdescribeEvaluations in interface AmazonMachineLearningAmazonMachineLearning.describeEvaluations(DescribeEvaluationsRequest)public DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest request)
AmazonMachineLearning
Returns a list of MLModel that match the search criteria in
the request.
describeMLModels in interface AmazonMachineLearningpublic DescribeMLModelsResult describeMLModels()
AmazonMachineLearningdescribeMLModels in interface AmazonMachineLearningAmazonMachineLearning.describeMLModels(DescribeMLModelsRequest)public GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest request)
AmazonMachineLearning
Returns a BatchPrediction that includes detailed metadata,
status, and data file information for a Batch Prediction
request.
getBatchPrediction in interface AmazonMachineLearningpublic GetDataSourceResult getDataSource(GetDataSourceRequest request)
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.
getDataSource in interface AmazonMachineLearningpublic GetEvaluationResult getEvaluation(GetEvaluationRequest request)
AmazonMachineLearning
Returns an Evaluation that includes metadata as well as the
current status of the Evaluation.
getEvaluation in interface AmazonMachineLearningpublic GetMLModelResult getMLModel(GetMLModelRequest request)
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.
getMLModel in interface AmazonMachineLearningpublic PredictResult predict(PredictRequest request)
AmazonMachineLearning
Generates a prediction for the observation using the specified
ML Model.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
predict in interface AmazonMachineLearningpublic UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest request)
AmazonMachineLearning
Updates the BatchPredictionName of a
BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
updateBatchPrediction in interface AmazonMachineLearningpublic UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest request)
AmazonMachineLearning
Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated data element.
updateDataSource in interface AmazonMachineLearningpublic UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest request)
AmazonMachineLearning
Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated data element.
updateEvaluation in interface AmazonMachineLearningpublic UpdateMLModelResult updateMLModel(UpdateMLModelRequest request)
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.
updateMLModel in interface AmazonMachineLearningpublic void shutdown()
AmazonMachineLearningshutdown in interface AmazonMachineLearningpublic ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
AmazonMachineLearningResponse 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.
getCachedResponseMetadata in interface AmazonMachineLearningrequest - The originally executed request.Copyright © 2013 Amazon Web Services, Inc. All Rights Reserved.