com.amazonaws.services.machinelearning.model

Class RedshiftDataSpec

java.lang.Object

com.amazonaws.services.machinelearning.model.RedshiftDataSpec

All Implemented Interfaces:

Serializable, Cloneable

public class RedshiftDataSpec
extends Object
implements Serializable, Cloneable

Describes the data specification of an Amazon Redshift DataSource.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RedshiftDataSpec()

Method Summary

Modifier and Type	Method and Description
RedshiftDataSpec	clone()
boolean	equals(Object obj)
RedshiftDatabaseCredentials	getDatabaseCredentials() Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
RedshiftDatabase	getDatabaseInformation() Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
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 Redshift DataSource.
String	getDataSchemaUri() Describes the schema location for an Amazon Redshift DataSource.
String	getS3StagingLocation() Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.
String	getSelectSqlQuery() Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.
int	hashCode()
void	setDatabaseCredentials(RedshiftDatabaseCredentials databaseCredentials) Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
void	setDatabaseInformation(RedshiftDatabase databaseInformation) Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
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 Redshift DataSource.
void	setDataSchemaUri(String dataSchemaUri) Describes the schema location for an Amazon Redshift DataSource.
void	setS3StagingLocation(String s3StagingLocation) Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.
void	setSelectSqlQuery(String selectSqlQuery) Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.
String	toString() Returns a string representation of this object; useful for testing and debugging.
RedshiftDataSpec	withDatabaseCredentials(RedshiftDatabaseCredentials databaseCredentials) Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
RedshiftDataSpec	withDatabaseInformation(RedshiftDatabase databaseInformation) Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
RedshiftDataSpec	withDataRearrangement(String dataRearrangement) A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource.
RedshiftDataSpec	withDataSchema(String dataSchema) A JSON string that represents the schema for an Amazon Redshift DataSource.
RedshiftDataSpec	withDataSchemaUri(String dataSchemaUri) Describes the schema location for an Amazon Redshift DataSource.
RedshiftDataSpec	withS3StagingLocation(String s3StagingLocation) Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.
RedshiftDataSpec	withSelectSqlQuery(String selectSqlQuery) Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

RedshiftDataSpec

public RedshiftDataSpec()

Method Detail

setDatabaseInformation

public void setDatabaseInformation(RedshiftDatabase databaseInformation)

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Parameters:

databaseInformation - Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

getDatabaseInformation

public RedshiftDatabase getDatabaseInformation()

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Returns:

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

withDatabaseInformation

public RedshiftDataSpec withDatabaseInformation(RedshiftDatabase databaseInformation)

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Parameters:

databaseInformation - Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Returns:

Returns a reference to this object so that method calls can be chained together.

setSelectSqlQuery

public void setSelectSqlQuery(String selectSqlQuery)

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Parameters:

selectSqlQuery - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

getSelectSqlQuery

public String getSelectSqlQuery()

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Returns:

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

withSelectSqlQuery

public RedshiftDataSpec withSelectSqlQuery(String selectSqlQuery)

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Parameters:

selectSqlQuery - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Returns:

Returns a reference to this object so that method calls can be chained together.

setDatabaseCredentials

public void setDatabaseCredentials(RedshiftDatabaseCredentials databaseCredentials)

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

Parameters:

databaseCredentials - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

getDatabaseCredentials

public RedshiftDatabaseCredentials getDatabaseCredentials()

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

Returns:

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

withDatabaseCredentials

public RedshiftDataSpec withDatabaseCredentials(RedshiftDatabaseCredentials databaseCredentials)

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

Parameters:

databaseCredentials - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

Returns:

Returns a reference to this object so that method calls can be chained together.

setS3StagingLocation

public void setS3StagingLocation(String s3StagingLocation)

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Parameters:

s3StagingLocation - Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

getS3StagingLocation

public String getS3StagingLocation()

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Returns:

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

withS3StagingLocation

public RedshiftDataSpec withS3StagingLocation(String s3StagingLocation)

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Parameters:

s3StagingLocation - Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Returns:

Returns a reference to this object so that method calls can be chained together.

setDataRearrangement

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"}}

Parameters:

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"}}

getDataRearrangement

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"}}

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"}}

withDataRearrangement

public RedshiftDataSpec 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"}}

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

setDataSchema

public void setDataSchema(String dataSchema)

A JSON string that represents the schema for an Amazon Redshift 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" ] }

Parameters:

dataSchema - A JSON string that represents the schema for an Amazon Redshift 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" ] }

getDataSchema

public String getDataSchema()

A JSON string that represents the schema for an Amazon Redshift 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 Redshift 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" ] }

withDataSchema

public RedshiftDataSpec withDataSchema(String dataSchema)

A JSON string that represents the schema for an Amazon Redshift 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" ] }

Parameters:

dataSchema - A JSON string that represents the schema for an Amazon Redshift 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:

Returns a reference to this object so that method calls can be chained together.

setDataSchemaUri

public void setDataSchemaUri(String dataSchemaUri)

Describes the schema location for an Amazon Redshift DataSource.

Parameters:

dataSchemaUri - Describes the schema location for an Amazon Redshift DataSource.

getDataSchemaUri

public String getDataSchemaUri()

Describes the schema location for an Amazon Redshift DataSource.

Returns:

Describes the schema location for an Amazon Redshift DataSource.

withDataSchemaUri

public RedshiftDataSpec withDataSchemaUri(String dataSchemaUri)

Describes the schema location for an Amazon Redshift DataSource.

Parameters:

dataSchemaUri - Describes the schema location for an Amazon Redshift DataSource.

Returns:

Returns a reference to this object so that method calls can be chained together.

toString

public String toString()

Returns a string representation of this object; useful for testing and debugging.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public RedshiftDataSpec clone()

Overrides:

clone in class Object