Copyright	(c) 2013-2023 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	Safe-Inferred
Language	Haskell2010

Amazonka.SageMaker.Types.ClarifyShapBaselineConfig

Description

Documentation

data ClarifyShapBaselineConfig Source #

The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm.

The number of records in the baseline data determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the __Synthetic data__ of Configure and create an endpoint.
ShapBaseline and ShapBaselineUri are mutually exclusive parameters. One or the either is required to configure a SHAP baseline.

See: newClarifyShapBaselineConfig smart constructor.

Constructors

ClarifyShapBaselineConfig'

Fields

mimeType :: Maybe Text
The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines'. Defaults to 'text/csv'.
shapBaseline :: Maybe Text
The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.
shapBaselineUri :: Maybe Text
The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.

Instances

Instances details

FromJSON ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods parseJSON :: Value -> Parser ClarifyShapBaselineConfig # parseJSONList :: Value -> Parser [ClarifyShapBaselineConfig] #
ToJSON ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods toJSON :: ClarifyShapBaselineConfig -> Value # toEncoding :: ClarifyShapBaselineConfig -> Encoding # toJSONList :: [ClarifyShapBaselineConfig] -> Value # toEncodingList :: [ClarifyShapBaselineConfig] -> Encoding #
Generic ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Associated Types type Rep ClarifyShapBaselineConfig :: Type -> Type # Methods from :: ClarifyShapBaselineConfig -> Rep ClarifyShapBaselineConfig x # to :: Rep ClarifyShapBaselineConfig x -> ClarifyShapBaselineConfig #
Read ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods readsPrec :: Int -> ReadS ClarifyShapBaselineConfig # readList :: ReadS [ClarifyShapBaselineConfig] # readPrec :: ReadPrec ClarifyShapBaselineConfig # readListPrec :: ReadPrec [ClarifyShapBaselineConfig] #
Show ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods showsPrec :: Int -> ClarifyShapBaselineConfig -> ShowS # show :: ClarifyShapBaselineConfig -> String # showList :: [ClarifyShapBaselineConfig] -> ShowS #
NFData ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods rnf :: ClarifyShapBaselineConfig -> () #
Eq ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods (==) :: ClarifyShapBaselineConfig -> ClarifyShapBaselineConfig -> Bool # (/=) :: ClarifyShapBaselineConfig -> ClarifyShapBaselineConfig -> Bool #
Hashable ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig Methods hashWithSalt :: Int -> ClarifyShapBaselineConfig -> Int # hash :: ClarifyShapBaselineConfig -> Int #
type Rep ClarifyShapBaselineConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.ClarifyShapBaselineConfig type Rep ClarifyShapBaselineConfig = D1 ('MetaData "ClarifyShapBaselineConfig" "Amazonka.SageMaker.Types.ClarifyShapBaselineConfig" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" 'False) (C1 ('MetaCons "ClarifyShapBaselineConfig'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mimeType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "shapBaseline") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "shapBaselineUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))

newClarifyShapBaselineConfig :: ClarifyShapBaselineConfig Source #

Create a value of ClarifyShapBaselineConfig 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:mimeType:ClarifyShapBaselineConfig', clarifyShapBaselineConfig_mimeType - The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines'. Defaults to 'text/csv'.

$sel:shapBaseline:ClarifyShapBaselineConfig', clarifyShapBaselineConfig_shapBaseline - The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

$sel:shapBaselineUri:ClarifyShapBaselineConfig', clarifyShapBaselineConfig_shapBaselineUri - The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.

clarifyShapBaselineConfig_mimeType :: Lens' ClarifyShapBaselineConfig (Maybe Text) Source #

The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines'. Defaults to 'text/csv'.

clarifyShapBaselineConfig_shapBaseline :: Lens' ClarifyShapBaselineConfig (Maybe Text) Source #

The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

clarifyShapBaselineConfig_shapBaselineUri :: Lens' ClarifyShapBaselineConfig (Maybe Text) Source #

The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.