@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class RDSDataSpec extends Object implements Serializable, Cloneable, StructuredPojo
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.
| Constructor and Description |
|---|
RDSDataSpec() |
| Modifier and Type | Method and Description |
|---|---|
RDSDataSpec |
clone() |
boolean |
equals(Object obj) |
RDSDatabaseCredentials |
getDatabaseCredentials()
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
|
RDSDatabase |
getDatabaseInformation()
Describes the
DatabaseName and InstanceIdentifier of an Amazon RDS database. |
String |
getDataRearrangement()
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource. |
String |
getDataSchema()
A JSON string that represents the schema for an Amazon RDS
DataSource. |
String |
getDataSchemaUri()
The Amazon S3 location of the
DataSchema. |
String |
getResourceRole()
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 |
getS3StagingLocation()
The Amazon S3 location for staging Amazon RDS data.
|
List<String> |
getSecurityGroupIds()
The security group IDs to be used to access a VPC-based RDS DB instance.
|
String |
getSelectSqlQuery()
The query that is used to retrieve the observation data for the
DataSource. |
String |
getServiceRole()
The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task
from Amazon RDS to Amazon S3.
|
String |
getSubnetId()
The subnet ID to be used to access a VPC-based RDS DB instance.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setDatabaseCredentials(RDSDatabaseCredentials databaseCredentials)
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
|
void |
setDatabaseInformation(RDSDatabase databaseInformation)
Describes the
DatabaseName and InstanceIdentifier of an Amazon RDS database. |
void |
setDataRearrangement(String dataRearrangement)
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource. |
void |
setDataSchema(String dataSchema)
A JSON string that represents the schema for an Amazon RDS
DataSource. |
void |
setDataSchemaUri(String dataSchemaUri)
The Amazon S3 location of the
DataSchema. |
void |
setResourceRole(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.
|
void |
setS3StagingLocation(String s3StagingLocation)
The Amazon S3 location for staging Amazon RDS data.
|
void |
setSecurityGroupIds(Collection<String> securityGroupIds)
The security group IDs to be used to access a VPC-based RDS DB instance.
|
void |
setSelectSqlQuery(String selectSqlQuery)
The query that is used to retrieve the observation data for the
DataSource. |
void |
setServiceRole(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.
|
void |
setSubnetId(String subnetId)
The subnet ID to be used to access a VPC-based RDS DB instance.
|
String |
toString()
Returns a string representation of this object.
|
RDSDataSpec |
withDatabaseCredentials(RDSDatabaseCredentials databaseCredentials)
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
|
RDSDataSpec |
withDatabaseInformation(RDSDatabase databaseInformation)
Describes the
DatabaseName and InstanceIdentifier of an Amazon RDS database. |
RDSDataSpec |
withDataRearrangement(String dataRearrangement)
A JSON string that represents the splitting and rearrangement processing to be applied to a
DataSource. |
RDSDataSpec |
withDataSchema(String dataSchema)
A JSON string that represents the schema for an Amazon RDS
DataSource. |
RDSDataSpec |
withDataSchemaUri(String dataSchemaUri)
The Amazon S3 location of the
DataSchema. |
RDSDataSpec |
withResourceRole(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.
|
RDSDataSpec |
withS3StagingLocation(String s3StagingLocation)
The Amazon S3 location for staging Amazon RDS data.
|
RDSDataSpec |
withSecurityGroupIds(Collection<String> securityGroupIds)
The security group IDs to be used to access a VPC-based RDS DB instance.
|
RDSDataSpec |
withSecurityGroupIds(String... securityGroupIds)
The security group IDs to be used to access a VPC-based RDS DB instance.
|
RDSDataSpec |
withSelectSqlQuery(String selectSqlQuery)
The query that is used to retrieve the observation data for the
DataSource. |
RDSDataSpec |
withServiceRole(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.
|
RDSDataSpec |
withSubnetId(String subnetId)
The subnet ID to be used to access a VPC-based RDS DB instance.
|
public void setDatabaseInformation(RDSDatabase databaseInformation)
Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
databaseInformation - Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.public RDSDatabase getDatabaseInformation()
Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
DatabaseName and InstanceIdentifier of an Amazon RDS database.public RDSDataSpec withDatabaseInformation(RDSDatabase databaseInformation)
Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
databaseInformation - Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.public void setSelectSqlQuery(String selectSqlQuery)
The query that is used to retrieve the observation data for the DataSource.
selectSqlQuery - The query that is used to retrieve the observation data for the DataSource.public String getSelectSqlQuery()
The query that is used to retrieve the observation data for the DataSource.
DataSource.public RDSDataSpec withSelectSqlQuery(String selectSqlQuery)
The query that is used to retrieve the observation data for the DataSource.
selectSqlQuery - The query that is used to retrieve the observation data for the DataSource.public void setDatabaseCredentials(RDSDatabaseCredentials databaseCredentials)
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
databaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.public RDSDatabaseCredentials getDatabaseCredentials()
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
public RDSDataSpec withDatabaseCredentials(RDSDatabaseCredentials databaseCredentials)
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
databaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.public void setS3StagingLocation(String s3StagingLocation)
The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
SelectSqlQuery is stored in this location.
s3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
SelectSqlQuery is stored in this location.public String getS3StagingLocation()
The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
SelectSqlQuery is stored in this location.
SelectSqlQuery is stored in this location.public RDSDataSpec withS3StagingLocation(String s3StagingLocation)
The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
SelectSqlQuery is stored in this location.
s3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using
SelectSqlQuery is stored in this location.public void setDataRearrangement(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"}}
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"}}
public String getDataRearrangement()
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"}}
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"}}
public RDSDataSpec withDataRearrangement(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"}}
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"}}
public void setDataSchema(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" ] }
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" ] }
public String getDataSchema()
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" ] }
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" ] }
public RDSDataSpec withDataSchema(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" ] }
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" ] }
public void setDataSchemaUri(String dataSchemaUri)
The Amazon S3 location of the DataSchema.
dataSchemaUri - The Amazon S3 location of the DataSchema.public String getDataSchemaUri()
The Amazon S3 location of the DataSchema.
DataSchema.public RDSDataSpec withDataSchemaUri(String dataSchemaUri)
The Amazon S3 location of the DataSchema.
dataSchemaUri - The Amazon S3 location of the DataSchema.public void setResourceRole(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.
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.public String getResourceRole()
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.
public RDSDataSpec withResourceRole(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.
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.public void setServiceRole(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.
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.public String getServiceRole()
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.
public RDSDataSpec withServiceRole(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.
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.public void setSubnetId(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.
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.public String getSubnetId()
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.
public RDSDataSpec withSubnetId(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.
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.public List<String> getSecurityGroupIds()
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.
public void setSecurityGroupIds(Collection<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.
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.public RDSDataSpec withSecurityGroupIds(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.
NOTE: This method appends the values to the existing list (if any). Use
setSecurityGroupIds(java.util.Collection) or withSecurityGroupIds(java.util.Collection) if you
want to override the existing values.
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.public RDSDataSpec withSecurityGroupIds(Collection<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.
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.public String toString()
toString in class ObjectObject.toString()public RDSDataSpec clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.