AWS SDK for C++  1.9.132
AWS SDK for C++
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Aws::MachineLearning::Model::RedshiftDataSpec Class Reference

#include <RedshiftDataSpec.h>

Public Member Functions

 RedshiftDataSpec ()
 
 RedshiftDataSpec (Aws::Utils::Json::JsonView jsonValue)
 
RedshiftDataSpecoperator= (Aws::Utils::Json::JsonView jsonValue)
 
Aws::Utils::Json::JsonValue Jsonize () const
 
const RedshiftDatabaseGetDatabaseInformation () const
 
bool DatabaseInformationHasBeenSet () const
 
void SetDatabaseInformation (const RedshiftDatabase &value)
 
void SetDatabaseInformation (RedshiftDatabase &&value)
 
RedshiftDataSpecWithDatabaseInformation (const RedshiftDatabase &value)
 
RedshiftDataSpecWithDatabaseInformation (RedshiftDatabase &&value)
 
const Aws::StringGetSelectSqlQuery () const
 
bool SelectSqlQueryHasBeenSet () const
 
void SetSelectSqlQuery (const Aws::String &value)
 
void SetSelectSqlQuery (Aws::String &&value)
 
void SetSelectSqlQuery (const char *value)
 
RedshiftDataSpecWithSelectSqlQuery (const Aws::String &value)
 
RedshiftDataSpecWithSelectSqlQuery (Aws::String &&value)
 
RedshiftDataSpecWithSelectSqlQuery (const char *value)
 
const RedshiftDatabaseCredentialsGetDatabaseCredentials () const
 
bool DatabaseCredentialsHasBeenSet () const
 
void SetDatabaseCredentials (const RedshiftDatabaseCredentials &value)
 
void SetDatabaseCredentials (RedshiftDatabaseCredentials &&value)
 
RedshiftDataSpecWithDatabaseCredentials (const RedshiftDatabaseCredentials &value)
 
RedshiftDataSpecWithDatabaseCredentials (RedshiftDatabaseCredentials &&value)
 
const Aws::StringGetS3StagingLocation () const
 
bool S3StagingLocationHasBeenSet () const
 
void SetS3StagingLocation (const Aws::String &value)
 
void SetS3StagingLocation (Aws::String &&value)
 
void SetS3StagingLocation (const char *value)
 
RedshiftDataSpecWithS3StagingLocation (const Aws::String &value)
 
RedshiftDataSpecWithS3StagingLocation (Aws::String &&value)
 
RedshiftDataSpecWithS3StagingLocation (const char *value)
 
const Aws::StringGetDataRearrangement () const
 
bool DataRearrangementHasBeenSet () const
 
void SetDataRearrangement (const Aws::String &value)
 
void SetDataRearrangement (Aws::String &&value)
 
void SetDataRearrangement (const char *value)
 
RedshiftDataSpecWithDataRearrangement (const Aws::String &value)
 
RedshiftDataSpecWithDataRearrangement (Aws::String &&value)
 
RedshiftDataSpecWithDataRearrangement (const char *value)
 
const Aws::StringGetDataSchema () const
 
bool DataSchemaHasBeenSet () const
 
void SetDataSchema (const Aws::String &value)
 
void SetDataSchema (Aws::String &&value)
 
void SetDataSchema (const char *value)
 
RedshiftDataSpecWithDataSchema (const Aws::String &value)
 
RedshiftDataSpecWithDataSchema (Aws::String &&value)
 
RedshiftDataSpecWithDataSchema (const char *value)
 
const Aws::StringGetDataSchemaUri () const
 
bool DataSchemaUriHasBeenSet () const
 
void SetDataSchemaUri (const Aws::String &value)
 
void SetDataSchemaUri (Aws::String &&value)
 
void SetDataSchemaUri (const char *value)
 
RedshiftDataSpecWithDataSchemaUri (const Aws::String &value)
 
RedshiftDataSpecWithDataSchemaUri (Aws::String &&value)
 
RedshiftDataSpecWithDataSchemaUri (const char *value)
 

Detailed Description

Describes the data specification of an Amazon Redshift DataSource.

See Also:

AWS API Reference

Definition at line 34 of file RedshiftDataSpec.h.

Constructor & Destructor Documentation

◆ RedshiftDataSpec() [1/2]

Aws::MachineLearning::Model::RedshiftDataSpec::RedshiftDataSpec ( )

◆ RedshiftDataSpec() [2/2]

Aws::MachineLearning::Model::RedshiftDataSpec::RedshiftDataSpec ( Aws::Utils::Json::JsonView  jsonValue)

Member Function Documentation

◆ DatabaseCredentialsHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::DatabaseCredentialsHasBeenSet ( ) const
inline

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

Definition at line 139 of file RedshiftDataSpec.h.

◆ DatabaseInformationHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::DatabaseInformationHasBeenSet ( ) const
inline

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Definition at line 53 of file RedshiftDataSpec.h.

◆ DataRearrangementHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::DataRearrangementHasBeenSet ( ) const
inline

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"}}

Definition at line 339 of file RedshiftDataSpec.h.

◆ DataSchemaHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::DataSchemaHasBeenSet ( ) const
inline

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" ] }

Definition at line 762 of file RedshiftDataSpec.h.

◆ DataSchemaUriHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::DataSchemaUriHasBeenSet ( ) const
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 907 of file RedshiftDataSpec.h.

◆ GetDatabaseCredentials()

const RedshiftDatabaseCredentials& Aws::MachineLearning::Model::RedshiftDataSpec::GetDatabaseCredentials ( ) const
inline

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

Definition at line 133 of file RedshiftDataSpec.h.

◆ GetDatabaseInformation()

const RedshiftDatabase& Aws::MachineLearning::Model::RedshiftDataSpec::GetDatabaseInformation ( ) const
inline

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Definition at line 47 of file RedshiftDataSpec.h.

◆ GetDataRearrangement()

const Aws::String& Aws::MachineLearning::Model::RedshiftDataSpec::GetDataRearrangement ( ) const
inline

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"}}

Definition at line 276 of file RedshiftDataSpec.h.

◆ GetDataSchema()

const Aws::String& Aws::MachineLearning::Model::RedshiftDataSpec::GetDataSchema ( ) const
inline

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" ] }

Definition at line 740 of file RedshiftDataSpec.h.

◆ GetDataSchemaUri()

const Aws::String& Aws::MachineLearning::Model::RedshiftDataSpec::GetDataSchemaUri ( ) const
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 901 of file RedshiftDataSpec.h.

◆ GetS3StagingLocation()

const Aws::String& Aws::MachineLearning::Model::RedshiftDataSpec::GetS3StagingLocation ( ) const
inline

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

Definition at line 170 of file RedshiftDataSpec.h.

◆ GetSelectSqlQuery()

const Aws::String& Aws::MachineLearning::Model::RedshiftDataSpec::GetSelectSqlQuery ( ) const
inline

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

Definition at line 84 of file RedshiftDataSpec.h.

◆ Jsonize()

Aws::Utils::Json::JsonValue Aws::MachineLearning::Model::RedshiftDataSpec::Jsonize ( ) const

◆ operator=()

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::operator= ( Aws::Utils::Json::JsonView  jsonValue)

◆ S3StagingLocationHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::S3StagingLocationHasBeenSet ( ) const
inline

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

Definition at line 176 of file RedshiftDataSpec.h.

◆ SelectSqlQueryHasBeenSet()

bool Aws::MachineLearning::Model::RedshiftDataSpec::SelectSqlQueryHasBeenSet ( ) const
inline

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

Definition at line 90 of file RedshiftDataSpec.h.

◆ SetDatabaseCredentials() [1/2]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDatabaseCredentials ( const RedshiftDatabaseCredentials value)
inline

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

Definition at line 145 of file RedshiftDataSpec.h.

◆ SetDatabaseCredentials() [2/2]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDatabaseCredentials ( RedshiftDatabaseCredentials &&  value)
inline

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

Definition at line 151 of file RedshiftDataSpec.h.

◆ SetDatabaseInformation() [1/2]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDatabaseInformation ( const RedshiftDatabase value)
inline

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Definition at line 59 of file RedshiftDataSpec.h.

◆ SetDatabaseInformation() [2/2]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDatabaseInformation ( RedshiftDatabase &&  value)
inline

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Definition at line 65 of file RedshiftDataSpec.h.

◆ SetDataRearrangement() [1/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataRearrangement ( Aws::String &&  value)
inline

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"}}

Definition at line 465 of file RedshiftDataSpec.h.

◆ SetDataRearrangement() [2/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataRearrangement ( const Aws::String value)
inline

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"}}

Definition at line 402 of file RedshiftDataSpec.h.

◆ SetDataRearrangement() [3/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataRearrangement ( const char *  value)
inline

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"}}

Definition at line 528 of file RedshiftDataSpec.h.

◆ SetDataSchema() [1/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataSchema ( Aws::String &&  value)
inline

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" ] }

Definition at line 806 of file RedshiftDataSpec.h.

◆ SetDataSchema() [2/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataSchema ( const Aws::String value)
inline

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" ] }

Definition at line 784 of file RedshiftDataSpec.h.

◆ SetDataSchema() [3/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataSchema ( const char *  value)
inline

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" ] }

Definition at line 828 of file RedshiftDataSpec.h.

◆ SetDataSchemaUri() [1/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataSchemaUri ( Aws::String &&  value)
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 919 of file RedshiftDataSpec.h.

◆ SetDataSchemaUri() [2/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataSchemaUri ( const Aws::String value)
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 913 of file RedshiftDataSpec.h.

◆ SetDataSchemaUri() [3/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetDataSchemaUri ( const char *  value)
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 925 of file RedshiftDataSpec.h.

◆ SetS3StagingLocation() [1/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetS3StagingLocation ( Aws::String &&  value)
inline

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

Definition at line 188 of file RedshiftDataSpec.h.

◆ SetS3StagingLocation() [2/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetS3StagingLocation ( const Aws::String value)
inline

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

Definition at line 182 of file RedshiftDataSpec.h.

◆ SetS3StagingLocation() [3/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetS3StagingLocation ( const char *  value)
inline

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

Definition at line 194 of file RedshiftDataSpec.h.

◆ SetSelectSqlQuery() [1/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetSelectSqlQuery ( Aws::String &&  value)
inline

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

Definition at line 102 of file RedshiftDataSpec.h.

◆ SetSelectSqlQuery() [2/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetSelectSqlQuery ( const Aws::String value)
inline

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

Definition at line 96 of file RedshiftDataSpec.h.

◆ SetSelectSqlQuery() [3/3]

void Aws::MachineLearning::Model::RedshiftDataSpec::SetSelectSqlQuery ( const char *  value)
inline

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

Definition at line 108 of file RedshiftDataSpec.h.

◆ WithDatabaseCredentials() [1/2]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDatabaseCredentials ( const RedshiftDatabaseCredentials value)
inline

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

Definition at line 157 of file RedshiftDataSpec.h.

◆ WithDatabaseCredentials() [2/2]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDatabaseCredentials ( RedshiftDatabaseCredentials &&  value)
inline

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

Definition at line 163 of file RedshiftDataSpec.h.

◆ WithDatabaseInformation() [1/2]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDatabaseInformation ( const RedshiftDatabase value)
inline

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Definition at line 71 of file RedshiftDataSpec.h.

◆ WithDatabaseInformation() [2/2]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDatabaseInformation ( RedshiftDatabase &&  value)
inline

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Definition at line 77 of file RedshiftDataSpec.h.

◆ WithDataRearrangement() [1/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataRearrangement ( Aws::String &&  value)
inline

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"}}

Definition at line 654 of file RedshiftDataSpec.h.

◆ WithDataRearrangement() [2/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataRearrangement ( const Aws::String value)
inline

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"}}

Definition at line 591 of file RedshiftDataSpec.h.

◆ WithDataRearrangement() [3/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataRearrangement ( const char *  value)
inline

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"}}

Definition at line 717 of file RedshiftDataSpec.h.

◆ WithDataSchema() [1/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataSchema ( Aws::String &&  value)
inline

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" ] }

Definition at line 872 of file RedshiftDataSpec.h.

◆ WithDataSchema() [2/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataSchema ( const Aws::String value)
inline

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" ] }

Definition at line 850 of file RedshiftDataSpec.h.

◆ WithDataSchema() [3/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataSchema ( const char *  value)
inline

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" ] }

Definition at line 894 of file RedshiftDataSpec.h.

◆ WithDataSchemaUri() [1/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataSchemaUri ( Aws::String &&  value)
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 937 of file RedshiftDataSpec.h.

◆ WithDataSchemaUri() [2/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataSchemaUri ( const Aws::String value)
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 931 of file RedshiftDataSpec.h.

◆ WithDataSchemaUri() [3/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithDataSchemaUri ( const char *  value)
inline

Describes the schema location for an Amazon Redshift DataSource.

Definition at line 943 of file RedshiftDataSpec.h.

◆ WithS3StagingLocation() [1/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithS3StagingLocation ( Aws::String &&  value)
inline

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

Definition at line 206 of file RedshiftDataSpec.h.

◆ WithS3StagingLocation() [2/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithS3StagingLocation ( const Aws::String value)
inline

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

Definition at line 200 of file RedshiftDataSpec.h.

◆ WithS3StagingLocation() [3/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithS3StagingLocation ( const char *  value)
inline

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

Definition at line 212 of file RedshiftDataSpec.h.

◆ WithSelectSqlQuery() [1/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithSelectSqlQuery ( Aws::String &&  value)
inline

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

Definition at line 120 of file RedshiftDataSpec.h.

◆ WithSelectSqlQuery() [2/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithSelectSqlQuery ( const Aws::String value)
inline

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

Definition at line 114 of file RedshiftDataSpec.h.

◆ WithSelectSqlQuery() [3/3]

RedshiftDataSpec& Aws::MachineLearning::Model::RedshiftDataSpec::WithSelectSqlQuery ( const char *  value)
inline

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

Definition at line 126 of file RedshiftDataSpec.h.


The documentation for this class was generated from the following file: