Struct aws_sdk_personalize::types::SolutionConfig

#[non_exhaustive]
pub struct SolutionConfig {
    pub event_value_threshold: Option<String>,
    pub hpo_config: Option<HpoConfig>,
    pub algorithm_hyper_parameters: Option<HashMap<String, String>>,
    pub feature_transformation_parameters: Option<HashMap<String, String>>,
    pub auto_ml_config: Option<AutoMlConfig>,
    pub optimization_objective: Option<OptimizationObjective>,
    pub training_data_config: Option<TrainingDataConfig>,
    pub auto_training_config: Option<AutoTrainingConfig>,
}

Describes the configuration properties for the solution.

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.

event_value_threshold: Option<String>

Only events with a value greater than or equal to this threshold are used for training a model.

hpo_config: Option<HpoConfig>

Describes the properties for hyperparameter optimization (HPO).

algorithm_hyper_parameters: Option<HashMap<String, String>>

Lists the algorithm hyperparameters and their values.

feature_transformation_parameters: Option<HashMap<String, String>>

Lists the feature transformation parameters.

auto_ml_config: Option<AutoMlConfig>

The AutoMLConfig object containing a list of recipes to search when AutoML is performed.

optimization_objective: Option<OptimizationObjective>

Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.

training_data_config: Option<TrainingDataConfig>

Specifies the training data configuration to use when creating a custom solution version (trained model).

auto_training_config: Option<AutoTrainingConfig>

Specifies the automatic training configuration to use.

Implementations

impl SolutionConfig

pub fn event_value_threshold(&self) -> Option<&str>

Only events with a value greater than or equal to this threshold are used for training a model.

pub fn hpo_config(&self) -> Option<&HpoConfig>

Describes the properties for hyperparameter optimization (HPO).

pub fn algorithm_hyper_parameters(&self) -> Option<&HashMap<String, String>>

Lists the algorithm hyperparameters and their values.

pub fn feature_transformation_parameters( &self ) -> Option<&HashMap<String, String>>

Lists the feature transformation parameters.

pub fn auto_ml_config(&self) -> Option<&AutoMlConfig>

The AutoMLConfig object containing a list of recipes to search when AutoML is performed.

pub fn optimization_objective(&self) -> Option<&OptimizationObjective>

Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.

pub fn training_data_config(&self) -> Option<&TrainingDataConfig>

Specifies the training data configuration to use when creating a custom solution version (trained model).

pub fn auto_training_config(&self) -> Option<&AutoTrainingConfig>

Specifies the automatic training configuration to use.

impl SolutionConfig

pub fn builder() -> SolutionConfigBuilder

Creates a new builder-style object to manufacture SolutionConfig.

Trait Implementations

impl Clone for SolutionConfig

fn clone(&self) -> SolutionConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for SolutionConfig

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

Formats the value using the given formatter.

impl PartialEq for SolutionConfig

fn eq(&self, other: &SolutionConfig) -> 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 SolutionConfig