@Generated(value="com.amazonaws:aws-java-sdk-code-generator") 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
.ENDPOINT_PREFIX
Modifier and Type | Method and Description |
---|---|
AddTagsResult |
addTags(AddTagsRequest request)
Adds one or more tags to an object, up to a limit of 10.
|
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 a database hosted on an Amazon Redshift cluster. |
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 DataSource 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 . |
DeleteTagsResult |
deleteTags(DeleteTagsRequest request)
Deletes the specified tags associated with an ML object.
|
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. |
DescribeTagsResult |
describeTags(DescribeTagsRequest request)
Describes one or more of the tags for your Amazon ML object.
|
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, data source information, and 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 . |
AmazonMachineLearningWaiters |
waiters() |
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: https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/java-dg-region-selection.html#region-selection- choose-endpoint
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 AmazonMachineLearning
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.public void setRegion(Region region)
AmazonMachineLearning
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.
setRegion
in interface AmazonMachineLearning
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.Region.getRegion(com.amazonaws.regions.Regions)
,
Region.createClient(Class, com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration)
,
Region.isServiceSupported(String)
public AddTagsResult addTags(AddTagsRequest request)
AmazonMachineLearning
Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you
add a tag using a key that is already associated with the ML object, AddTags
updates the tag's
value.
addTags
in interface AmazonMachineLearning
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 AmazonMachineLearning
public 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
the COMPLETED
or PENDING
state can be used only to perform
>CreateMLModel
>, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
createDataSourceFromRDS
in interface AmazonMachineLearning
public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)
AmazonMachineLearning
Creates a DataSource
from a database hosted on an Amazon Redshift cluster. A DataSource
references data that can be used to perform either CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to
CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the
DataSource
status to PENDING
. After the DataSource
is created and ready
for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in
COMPLETED
or PENDING
states can be used to perform only CreateMLModel
,
CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
by a SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to
transfer the result set of the SelectSqlQuery
query to S3StagingLocation
.
After the DataSource
has been created, it's ready for use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
requires a recipe. A recipe describes how each input variable will be used in training an MLModel
.
Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it
be combined with another variable or will it be split apart into word combinations? The recipe provides answers
to these questions.
You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon
Redshift datasource to create a new datasource. To do so, call GetDataSource
for an existing
datasource and copy the values to a CreateDataSource
call. Change the settings that you want to
change and make sure that all required fields have the appropriate values.
createDataSourceFromRedshift
in interface AmazonMachineLearning
public 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
has been created and is
ready for use, Amazon ML sets the Status
parameter to COMPLETED
.
DataSource
in the COMPLETED
or PENDING
state can be used to perform only
CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and
includes an error message in the Message
attribute of the GetDataSource
operation
response.
The observation data used in a DataSource
should be ready to use; that is, it should have a
consistent structure, and missing data values should be kept to a minimum. The observation data must reside in
one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that
describes the data items by name and type. The same schema must be used for all of the data files referenced by
the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If
you plan to use the DataSource
to train an MLModel
, the DataSource
also
needs a recipe. A recipe describes how each input variable will be used in training an MLModel
. Will
the variable be included or excluded from training? Will the variable be manipulated; for example, will it be
combined with another variable or will it be split apart into word combinations? The recipe provides answers to
these questions.
createDataSourceFromS3
in interface AmazonMachineLearning
public 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 AmazonMachineLearning
public CreateMLModelResult createMLModel(CreateMLModelRequest request)
AmazonMachineLearning
Creates a new MLModel
using the DataSource
and the recipe as information sources.
An MLModel
is nearly immutable. Users can update only the MLModelName
and the
ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon
Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
has been created and ready is for use, Amazon ML sets the status to
COMPLETED
.
You can use the GetMLModel
operation to check the progress of the MLModel
during the
creation operation.
CreateMLModel
requires a DataSource
with computed statistics, which can be created by
setting ComputeStatistics
to true
in CreateDataSourceFromRDS
,
CreateDataSourceFromS3
, or CreateDataSourceFromRedshift
operations.
createMLModel
in interface AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)
AmazonMachineLearning
Deletes a real time endpoint of an MLModel
.
deleteRealtimeEndpoint
in interface AmazonMachineLearning
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
AmazonMachineLearning
Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.
If you specify a tag that doesn't exist, Amazon ML ignores it.
deleteTags
in interface AmazonMachineLearning
public DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest request)
AmazonMachineLearning
Returns a list of BatchPrediction
operations that match the search criteria in the request.
describeBatchPredictions
in interface AmazonMachineLearning
public DescribeBatchPredictionsResult describeBatchPredictions()
AmazonMachineLearning
describeBatchPredictions
in interface AmazonMachineLearning
AmazonMachineLearning.describeBatchPredictions(DescribeBatchPredictionsRequest)
public DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest request)
AmazonMachineLearning
Returns a list of DataSource
that match the search criteria in the request.
describeDataSources
in interface AmazonMachineLearning
public DescribeDataSourcesResult describeDataSources()
AmazonMachineLearning
describeDataSources
in interface AmazonMachineLearning
AmazonMachineLearning.describeDataSources(DescribeDataSourcesRequest)
public DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest request)
AmazonMachineLearning
Returns a list of DescribeEvaluations
that match the search criteria in the request.
describeEvaluations
in interface AmazonMachineLearning
public DescribeEvaluationsResult describeEvaluations()
AmazonMachineLearning
describeEvaluations
in interface AmazonMachineLearning
AmazonMachineLearning.describeEvaluations(DescribeEvaluationsRequest)
public DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest request)
AmazonMachineLearning
Returns a list of MLModel
that match the search criteria in the request.
describeMLModels
in interface AmazonMachineLearning
public DescribeMLModelsResult describeMLModels()
AmazonMachineLearning
describeMLModels
in interface AmazonMachineLearning
AmazonMachineLearning.describeMLModels(DescribeMLModelsRequest)
public DescribeTagsResult describeTags(DescribeTagsRequest request)
AmazonMachineLearning
Describes one or more of the tags for your Amazon ML object.
describeTags
in interface AmazonMachineLearning
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 AmazonMachineLearning
public 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 AmazonMachineLearning
public GetEvaluationResult getEvaluation(GetEvaluationRequest request)
AmazonMachineLearning
Returns an Evaluation
that includes metadata as well as the current status of the
Evaluation
.
getEvaluation
in interface AmazonMachineLearning
public GetMLModelResult getMLModel(GetMLModelRequest request)
AmazonMachineLearning
Returns an MLModel
that includes detailed metadata, data source information, and the current status
of the MLModel
.
GetMLModel
provides results in normal or verbose format.
getMLModel
in interface AmazonMachineLearning
public PredictResult predict(PredictRequest request)
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.
predict
in interface AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public 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 AmazonMachineLearning
public void shutdown()
AmazonMachineLearning
shutdown
in interface AmazonMachineLearning
public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
AmazonMachineLearning
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.
getCachedResponseMetadata
in interface AmazonMachineLearning
request
- The originally executed request.public AmazonMachineLearningWaiters waiters()
waiters
in interface AmazonMachineLearning