public class CreateDataSourceFromS3Request extends AmazonWebServiceRequest implements Serializable, Cloneable
CreateDataSourceFromS3 operation.
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
.
NOOP| Constructor and Description |
|---|
CreateDataSourceFromS3Request() |
| Modifier and Type | Method and Description |
|---|---|
CreateDataSourceFromS3Request |
clone()
Creates a shallow clone of this request.
|
boolean |
equals(Object obj) |
Boolean |
getComputeStatistics()
The compute statistics for a
DataSource. |
String |
getDataSourceId()
A user-supplied identifier that uniquely identifies the
DataSource. |
String |
getDataSourceName()
A user-supplied name or description of the
DataSource. |
S3DataSpec |
getDataSpec()
The data specification of a
DataSource:
|
int |
hashCode() |
Boolean |
isComputeStatistics()
The compute statistics for a
DataSource. |
void |
setComputeStatistics(Boolean computeStatistics)
The compute statistics for a
DataSource. |
void |
setDataSourceId(String dataSourceId)
A user-supplied identifier that uniquely identifies the
DataSource. |
void |
setDataSourceName(String dataSourceName)
A user-supplied name or description of the
DataSource. |
void |
setDataSpec(S3DataSpec dataSpec)
The data specification of a
DataSource:
|
String |
toString()
Returns a string representation of this object; useful for testing and
debugging.
|
CreateDataSourceFromS3Request |
withComputeStatistics(Boolean computeStatistics)
The compute statistics for a
DataSource. |
CreateDataSourceFromS3Request |
withDataSourceId(String dataSourceId)
A user-supplied identifier that uniquely identifies the
DataSource. |
CreateDataSourceFromS3Request |
withDataSourceName(String dataSourceName)
A user-supplied name or description of the
DataSource. |
CreateDataSourceFromS3Request |
withDataSpec(S3DataSpec dataSpec)
The data specification of a
DataSource:
|
copyBaseTo, getCustomRequestHeaders, getGeneralProgressListener, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestMetricCollector, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestMetricCollector, withGeneralProgressListener, withRequestMetricCollectorpublic String getDataSourceId()
DataSource.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
DataSource.public void setDataSourceId(String dataSourceId)
DataSource.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
dataSourceId - A user-supplied identifier that uniquely identifies the
DataSource.public CreateDataSourceFromS3Request withDataSourceId(String dataSourceId)
DataSource.
Returns a reference to this object so that method calls can be chained together.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
dataSourceId - A user-supplied identifier that uniquely identifies the
DataSource.public String getDataSourceName()
DataSource.
Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$
DataSource.public void setDataSourceName(String dataSourceName)
DataSource.
Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$
dataSourceName - A user-supplied name or description of the DataSource.public CreateDataSourceFromS3Request withDataSourceName(String dataSourceName)
DataSource.
Returns a reference to this object so that method calls can be chained together.
Constraints:
Length: 0 - 1024
Pattern: .*\S.*|^$
dataSourceName - A user-supplied name or description of the DataSource.public S3DataSpec getDataSpec()
DataSource: DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 -
Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not
required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting
requirement of a Datasource.
Sample -
"{\"randomSeed\":\"some-random-seed\",
\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
DataSource: DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 -
Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not
required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting
requirement of a Datasource.
Sample -
"{\"randomSeed\":\"some-random-seed\",
\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
public void setDataSpec(S3DataSpec dataSpec)
DataSource: DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 -
Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not
required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting
requirement of a Datasource.
Sample -
"{\"randomSeed\":\"some-random-seed\",
\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
dataSpec - The data specification of a DataSource: DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 -
Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not
required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting
requirement of a Datasource.
Sample -
"{\"randomSeed\":\"some-random-seed\",
\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
public CreateDataSourceFromS3Request withDataSpec(S3DataSpec dataSpec)
DataSource: DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 -
Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not
required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting
requirement of a Datasource.
Sample -
"{\"randomSeed\":\"some-random-seed\",
\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
Returns a reference to this object so that method calls can be chained together.
dataSpec - The data specification of a DataSource: DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 -
Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not
required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting
requirement of a Datasource.
Sample -
"{\"randomSeed\":\"some-random-seed\",
\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"
public Boolean isComputeStatistics()
DataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingDataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingpublic void setComputeStatistics(Boolean computeStatistics)
DataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingcomputeStatistics - The compute statistics for a DataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingpublic CreateDataSourceFromS3Request withComputeStatistics(Boolean computeStatistics)
DataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel training
Returns a reference to this object so that method calls can be chained together.
computeStatistics - The compute statistics for a DataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingpublic Boolean getComputeStatistics()
DataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingDataSource. The statistics
are generated from the observation data referenced by a
DataSource. Amazon ML uses the statistics internally
during an MLModel training. This parameter must be set to
true if the DataSource needs to be used for
MLModel trainingpublic String toString()
toString in class ObjectObject.toString()public CreateDataSourceFromS3Request clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()Copyright © 2015. All rights reserved.