Struct aws_sdk_personalize::types::TrainingDataConfig

#[non_exhaustive]
pub struct TrainingDataConfig {
    pub excluded_dataset_columns: Option<HashMap<String, Vec<String>>>,
}

The training data configuration to use when creating a domain recommender or custom solution version (trained model).

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.

excluded_dataset_columns: Option<HashMap<String, Vec<String>>>

Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations. For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.

Implementations

impl TrainingDataConfig

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

Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations. For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.

impl TrainingDataConfig

pub fn builder() -> TrainingDataConfigBuilder

Creates a new builder-style object to manufacture TrainingDataConfig.

Trait Implementations

impl Clone for TrainingDataConfig

fn clone(&self) -> TrainingDataConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingDataConfig

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

Formats the value using the given formatter.

impl PartialEq for TrainingDataConfig

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