Struct aws_sdk_sagemaker::types::ClarifyShapConfig

#[non_exhaustive]
pub struct ClarifyShapConfig {
    pub shap_baseline_config: Option<ClarifyShapBaselineConfig>,
    pub number_of_samples: Option<i32>,
    pub use_logit: Option<bool>,
    pub seed: Option<i32>,
    pub text_config: Option<ClarifyTextConfig>,
}

The configuration for SHAP analysis using SageMaker Clarify Explainer.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

shap_baseline_config: Option<ClarifyShapBaselineConfig>

The configuration for the SHAP baseline of the Kernal SHAP algorithm.

number_of_samples: Option<i32>

The number of samples to be used for analysis by the Kernal SHAP algorithm.

The number of samples 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.

use_logit: Option<bool>

A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

seed: Option<i32>

The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

text_config: Option<ClarifyTextConfig>

A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

Implementations

impl ClarifyShapConfig

pub fn shap_baseline_config(&self) -> Option<&ClarifyShapBaselineConfig>

The configuration for the SHAP baseline of the Kernal SHAP algorithm.

pub fn number_of_samples(&self) -> Option<i32>

The number of samples to be used for analysis by the Kernal SHAP algorithm.

The number of samples 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.

pub fn use_logit(&self) -> Option<bool>

A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

pub fn seed(&self) -> Option<i32>

The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

pub fn text_config(&self) -> Option<&ClarifyTextConfig>

A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

impl ClarifyShapConfig

pub fn builder() -> ClarifyShapConfigBuilder

Creates a new builder-style object to manufacture ClarifyShapConfig.

Trait Implementations

impl Clone for ClarifyShapConfig

fn clone(&self) -> ClarifyShapConfig

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ClarifyShapConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<ClarifyShapConfig> for ClarifyShapConfig

fn eq(&self, other: &ClarifyShapConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ClarifyShapConfig