software.amazon.awssdk.services.machinelearning.model

Class RDSDataSpec

java.lang.Object

software.amazon.awssdk.services.machinelearning.model.RDSDataSpec

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<RDSDataSpec.Builder,RDSDataSpec>

@Generated(value="software.amazon.awssdk:codegen")
public final class RDSDataSpec
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<RDSDataSpec.Builder,RDSDataSpec>

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	RDSDataSpec.Builder

Method Summary

Modifier and Type	Method and Description
static RDSDataSpec.Builder	builder()
RDSDatabaseCredentials	databaseCredentials() The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
RDSDatabase	databaseInformation() Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
String	dataRearrangement() A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource.
String	dataSchema() A JSON string that represents the schema for an Amazon RDS DataSource.
String	dataSchemaUri() The Amazon S3 location of the DataSchema.
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
boolean	hasSecurityGroupIds() Returns true if the SecurityGroupIds property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty).
String	resourceRole() The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task.
String	s3StagingLocation() The Amazon S3 location for staging Amazon RDS data.
List<SdkField<?>>	sdkFields()
List<String>	securityGroupIds() The security group IDs to be used to access a VPC-based RDS DB instance.
String	selectSqlQuery() The query that is used to retrieve the observation data for the DataSource.
static Class<? extends RDSDataSpec.Builder>	serializableBuilderClass()
String	serviceRole() The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3.
String	subnetId() The subnet ID to be used to access a VPC-based RDS DB instance.
RDSDataSpec.Builder	toBuilder()
String	toString() Returns a string representation of this object.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

databaseInformation

public RDSDatabase databaseInformation()

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

Returns:

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

selectSqlQuery

public String selectSqlQuery()

The query that is used to retrieve the observation data for the DataSource.

Returns:

The query that is used to retrieve the observation data for the DataSource.

databaseCredentials

public RDSDatabaseCredentials databaseCredentials()

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

Returns:

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

s3StagingLocation

public String s3StagingLocation()

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

Returns:

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

dataRearrangement

public String dataRearrangement()

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

• percentBegin

  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

• percentEnd

  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

• complement

  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

  Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

  Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

• strategy

  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

  Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

  The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

  Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

Returns:

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

• percentBegin

  Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

• percentEnd

  Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

• complement

  The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

  For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

  Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

  Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

• strategy

  To change how Amazon ML splits the data for a datasource, use the strategy parameter.

  The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

  The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

  Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

  To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

  The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

  Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

  Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

dataSchema

public String dataSchema()

A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

Returns:

A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

dataSchemaUri

public String dataSchemaUri()

The Amazon S3 location of the DataSchema.

Returns:

The Amazon S3 location of the DataSchema.

resourceRole

public String resourceRole()

The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.

Returns:

The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.

serviceRole

public String serviceRole()

The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

Returns:

The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

subnetId

public String subnetId()

The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

Returns:

The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

hasSecurityGroupIds

public boolean hasSecurityGroupIds()

Returns true if the SecurityGroupIds property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty). For responses returned by the SDK, the sender is the AWS service.

securityGroupIds

public List<String> securityGroupIds()

The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

You can use hasSecurityGroupIds() to see if a value was sent in this field.

Returns:

The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

toBuilder

public RDSDataSpec.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<RDSDataSpec.Builder,RDSDataSpec>

builder

public static RDSDataSpec.Builder builder()

serializableBuilderClass

public static Class<? extends RDSDataSpec.Builder> serializableBuilderClass()

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

getValueForField

public <T> Optional<T> getValueForField(String fieldName,
                                        Class<T> clazz)

sdkFields

public List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo