libZSservicesZSamazonka-mlZSamazonka-ml
Copyright(c) 2013-2021 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone

Amazonka.MachineLearning.Types.RedshiftDataSpec

Description

 
Synopsis

Documentation

data RedshiftDataSpec Source #

Describes the data specification of an Amazon Redshift DataSource.

See: newRedshiftDataSpec smart constructor.

Constructors

RedshiftDataSpec' 

Fields

  • dataSchemaUri :: Maybe Text

    Describes the schema location for an Amazon Redshift DataSource.

  • dataSchema :: Maybe Text

    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" ] }

  • dataRearrangement :: Maybe Text

    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"}}

  • databaseInformation :: RedshiftDatabase

    Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

  • selectSqlQuery :: Text

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

  • databaseCredentials :: RedshiftDatabaseCredentials

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

  • s3StagingLocation :: Text

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

Instances

Instances details
Eq RedshiftDataSpec Source # 
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Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec

Read RedshiftDataSpec Source # 
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Show RedshiftDataSpec Source # 
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Generic RedshiftDataSpec Source # 
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Associated Types

type Rep RedshiftDataSpec :: Type -> Type #

NFData RedshiftDataSpec Source # 
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Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec

Methods

rnf :: RedshiftDataSpec -> () #

Hashable RedshiftDataSpec Source # 
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Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec

ToJSON RedshiftDataSpec Source # 
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type Rep RedshiftDataSpec Source # 
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Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec

type Rep RedshiftDataSpec = D1 ('MetaData "RedshiftDataSpec" "Amazonka.MachineLearning.Types.RedshiftDataSpec" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RedshiftDataSpec'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "dataSchemaUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 ('MetaSel ('Just "dataSchema") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "dataRearrangement") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :*: ((S1 ('MetaSel ('Just "databaseInformation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RedshiftDatabase) :*: S1 ('MetaSel ('Just "selectSqlQuery") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :*: (S1 ('MetaSel ('Just "databaseCredentials") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RedshiftDatabaseCredentials) :*: S1 ('MetaSel ('Just "s3StagingLocation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))

newRedshiftDataSpec Source #

Create a value of RedshiftDataSpec with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSchemaUri:RedshiftDataSpec', redshiftDataSpec_dataSchemaUri - Describes the schema location for an Amazon Redshift DataSource.

$sel:dataSchema:RedshiftDataSpec', redshiftDataSpec_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" ] }

$sel:dataRearrangement:RedshiftDataSpec', redshiftDataSpec_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"}}

$sel:databaseInformation:RedshiftDataSpec', redshiftDataSpec_databaseInformation - Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

$sel:selectSqlQuery:RedshiftDataSpec', redshiftDataSpec_selectSqlQuery - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

$sel:databaseCredentials:RedshiftDataSpec', redshiftDataSpec_databaseCredentials - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

$sel:s3StagingLocation:RedshiftDataSpec', redshiftDataSpec_s3StagingLocation - Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

redshiftDataSpec_dataSchemaUri :: Lens' RedshiftDataSpec (Maybe Text) Source #

Describes the schema location for an Amazon Redshift DataSource.

redshiftDataSpec_dataSchema :: Lens' RedshiftDataSpec (Maybe Text) Source #

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" ] }

redshiftDataSpec_dataRearrangement :: Lens' RedshiftDataSpec (Maybe Text) Source #

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"}}

redshiftDataSpec_databaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase Source #

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

redshiftDataSpec_selectSqlQuery :: Lens' RedshiftDataSpec Text Source #

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

redshiftDataSpec_databaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials Source #

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

redshiftDataSpec_s3StagingLocation :: Lens' RedshiftDataSpec Text Source #

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