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RedshiftDataSpec

import "github.com/aws/aws-sdk-go/service/machinelearning"

type RedshiftDataSpec struct { DataRearrangement *string `type:"string"` DataSchema *string `type:"string"` DataSchemaUri *string `type:"string"` DatabaseCredentials *RedshiftDatabaseCredentials `type:"structure" required:"true"` DatabaseInformation *RedshiftDatabase `type:"structure" required:"true"` S3StagingLocation *string `type:"string" required:"true"` SelectSqlQuery *string `min:"1" type:"string" required:"true"` }

Describes the data specification of an Amazon Redshift DataSource.

DataRearrangement

Type: *string

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

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

  • percentBegin

Use percentBegin to indicate the beginning of the range of the data used

to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
  • percentEnd

Use percentEnd to indicate the end of the range of the data used to create

the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
  • complement

The complement parameter instructs Amazon ML to use the data that is not

included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

For example, the following two datasources do not share any data, and can

be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

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

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

"complement":"true"}}
  • strategy

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

parameter.

The default value for the strategy parameter is sequential, meaning that

Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

The following two DataRearrangement lines are examples of sequentially ordered

training and evaluation datasources:

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

"strategy":"sequential"}}

Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,

"strategy":"sequential", "complement":"true"}}

To randomly split the input data into the proportions indicated by the percentBegin

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

The following two DataRearrangement lines are examples of non-sequentially

ordered training and evaluation datasources:

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

"strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100,

"strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}
DataSchema

Type: *string

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" ] }

DataSchemaUri

Type: *string

Describes the schema location for an Amazon Redshift DataSource.

DatabaseCredentials

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

DatabaseInformation

Describes the database details required to connect to an Amazon Redshift database.

S3StagingLocation

Type: *string

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

S3StagingLocation is a required field

SelectSqlQuery

Type: *string

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

SelectSqlQuery is a required field

Method

GoString

func (s RedshiftDataSpec) GoString() string

GoString returns the string representation

SetDataRearrangement

func (s *RedshiftDataSpec) SetDataRearrangement(v string) *RedshiftDataSpec

SetDataRearrangement sets the DataRearrangement field's value.

SetDataSchema

func (s *RedshiftDataSpec) SetDataSchema(v string) *RedshiftDataSpec

SetDataSchema sets the DataSchema field's value.

SetDataSchemaUri

func (s *RedshiftDataSpec) SetDataSchemaUri(v string) *RedshiftDataSpec

SetDataSchemaUri sets the DataSchemaUri field's value.

SetDatabaseCredentials

func (s *RedshiftDataSpec) SetDatabaseCredentials(v *RedshiftDatabaseCredentials) *RedshiftDataSpec

SetDatabaseCredentials sets the DatabaseCredentials field's value.

SetDatabaseInformation

func (s *RedshiftDataSpec) SetDatabaseInformation(v *RedshiftDatabase) *RedshiftDataSpec

SetDatabaseInformation sets the DatabaseInformation field's value.

SetS3StagingLocation

func (s *RedshiftDataSpec) SetS3StagingLocation(v string) *RedshiftDataSpec

SetS3StagingLocation sets the S3StagingLocation field's value.

SetSelectSqlQuery

func (s *RedshiftDataSpec) SetSelectSqlQuery(v string) *RedshiftDataSpec

SetSelectSqlQuery sets the SelectSqlQuery field's value.

String

func (s RedshiftDataSpec) String() string

String returns the string representation

Validate

func (s *RedshiftDataSpec) Validate() error

Validate inspects the fields of the type to determine if they are valid.

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