/AWS1/CL_ML_RDSDATASPEC

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

CONSTRUCTOR

IMPORTING

Required arguments:

io_databaseinformation TYPE REF TO /AWS1/CL_ML_RDSDATABASE /AWS1/CL_ML_RDSDATABASE

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

iv_selectsqlquery TYPE /AWS1/ML_RDSSELECTSQLQUERY /AWS1/ML_RDSSELECTSQLQUERY

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

io_databasecredentials TYPE REF TO /AWS1/CL_ML_RDSDATABASECREDS /AWS1/CL_ML_RDSDATABASECREDS

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

iv_s3staginglocation TYPE /AWS1/ML_S3URL /AWS1/ML_S3URL

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

iv_resourcerole TYPE /AWS1/ML_EDPRESOURCEROLE /AWS1/ML_EDPRESOURCEROLE

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

iv_servicerole TYPE /AWS1/ML_EDPSERVICEROLE /AWS1/ML_EDPSERVICEROLE

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

iv_subnetid TYPE /AWS1/ML_EDPSUBNETID /AWS1/ML_EDPSUBNETID

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

it_securitygroupids TYPE /AWS1/CL_ML_EDPSECGROUPIDS_W=>TT_EDPSECURITYGROUPIDS TT_EDPSECURITYGROUPIDS

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

Optional arguments:

iv_datarearrangement TYPE /AWS1/ML_DATAREARRANGEMENT /AWS1/ML_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"}}

iv_dataschema TYPE /AWS1/ML_DATASCHEMA /AWS1/ML_DATASCHEMA

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

A DataSchema is not required if you specify a DataSchemaUri

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

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

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

"excludedVariableNames": [ "F6" ] }

iv_dataschemauri TYPE /AWS1/ML_S3URL /AWS1/ML_S3URL

The Amazon S3 location of the DataSchema.

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Queryable Attributes

DatabaseInformation

Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

Accessible with the following methods

Method	Description
GET_DATABASEINFORMATION()	Getter for DATABASEINFORMATION

SelectSqlQuery

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

Accessible with the following methods

Method	Description
GET_SELECTSQLQUERY()	Getter for SELECTSQLQUERY, with configurable default
ASK_SELECTSQLQUERY()	Getter for SELECTSQLQUERY w/ exceptions if field has no valu
HAS_SELECTSQLQUERY()	Determine if SELECTSQLQUERY has a value

DatabaseCredentials

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

Accessible with the following methods

Method	Description
GET_DATABASECREDENTIALS()	Getter for DATABASECREDENTIALS

S3StagingLocation

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

Accessible with the following methods

Method	Description
GET_S3STAGINGLOCATION()	Getter for S3STAGINGLOCATION, with configurable default
ASK_S3STAGINGLOCATION()	Getter for S3STAGINGLOCATION w/ exceptions if field has no v
HAS_S3STAGINGLOCATION()	Determine if S3STAGINGLOCATION has a value

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"}}

Accessible with the following methods

Method	Description
GET_DATAREARRANGEMENT()	Getter for DATAREARRANGEMENT, with configurable default
ASK_DATAREARRANGEMENT()	Getter for DATAREARRANGEMENT w/ exceptions if field has no v
HAS_DATAREARRANGEMENT()	Determine if DATAREARRANGEMENT has a value

DataSchema

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

A DataSchema is not required if you specify a DataSchemaUri

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

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

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

"excludedVariableNames": [ "F6" ] }

Accessible with the following methods

Method	Description
GET_DATASCHEMA()	Getter for DATASCHEMA, with configurable default
ASK_DATASCHEMA()	Getter for DATASCHEMA w/ exceptions if field has no value
HAS_DATASCHEMA()	Determine if DATASCHEMA has a value

DataSchemaUri

The Amazon S3 location of the DataSchema.

Accessible with the following methods

Method	Description
GET_DATASCHEMAURI()	Getter for DATASCHEMAURI, with configurable default
ASK_DATASCHEMAURI()	Getter for DATASCHEMAURI w/ exceptions if field has no value
HAS_DATASCHEMAURI()	Determine if DATASCHEMAURI has a value

ResourceRole

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

Accessible with the following methods

Method	Description
GET_RESOURCEROLE()	Getter for RESOURCEROLE, with configurable default
ASK_RESOURCEROLE()	Getter for RESOURCEROLE w/ exceptions if field has no value
HAS_RESOURCEROLE()	Determine if RESOURCEROLE has a value

ServiceRole

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

Accessible with the following methods

Method	Description
GET_SERVICEROLE()	Getter for SERVICEROLE, with configurable default
ASK_SERVICEROLE()	Getter for SERVICEROLE w/ exceptions if field has no value
HAS_SERVICEROLE()	Determine if SERVICEROLE has a value

SubnetId

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

Accessible with the following methods

Method	Description
GET_SUBNETID()	Getter for SUBNETID, with configurable default
ASK_SUBNETID()	Getter for SUBNETID w/ exceptions if field has no value
HAS_SUBNETID()	Determine if SUBNETID has a value

SecurityGroupIds

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

Accessible with the following methods

Method	Description
GET_SECURITYGROUPIDS()	Getter for SECURITYGROUPIDS, with configurable default
ASK_SECURITYGROUPIDS()	Getter for SECURITYGROUPIDS w/ exceptions if field has no va
HAS_SECURITYGROUPIDS()	Determine if SECURITYGROUPIDS has a value