Struct aws_sdk_personalize::operation::create_solution::CreateSolutionInput

#[non_exhaustive]
pub struct CreateSolutionInput {
    pub name: Option<String>,
    pub perform_hpo: Option<bool>,
    pub perform_auto_ml: Option<bool>,
    pub recipe_arn: Option<String>,
    pub dataset_group_arn: Option<String>,
    pub event_type: Option<String>,
    pub solution_config: Option<SolutionConfig>,
    pub tags: Option<Vec<Tag>>,
}

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.

name: Option<String>

The name for the solution.

perform_hpo: Option<bool>

Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.

When performing AutoML, this parameter is always true and you should not set it to false.

perform_auto_ml: Option<bool>

We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Determining your use case.

Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.

When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

recipe_arn: Option<String>

The ARN of the recipe to use for model training. This is required when performAutoML is false.

dataset_group_arn: Option<String>

The Amazon Resource Name (ARN) of the dataset group that provides the training data.

event_type: Option<String>

When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.

solution_config: Option<SolutionConfig>

The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.

Amazon Personalize doesn't support configuring the hpoObjective at this time.

tags: Option<Vec<Tag>>

A list of tags to apply to the solution.

Implementations

impl CreateSolutionInput

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

The name for the solution.

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

Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.

When performing AutoML, this parameter is always true and you should not set it to false.

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

We don't recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Determining your use case.

Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.

When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.

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

The ARN of the recipe to use for model training. This is required when performAutoML is false.

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

The Amazon Resource Name (ARN) of the dataset group that provides the training data.

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

When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.

If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.

pub fn solution_config(&self) -> Option<&SolutionConfig>

The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.

Amazon Personalize doesn't support configuring the hpoObjective at this time.

pub fn tags(&self) -> &[Tag]

A list of tags to apply to the solution.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

impl CreateSolutionInput

pub fn builder() -> CreateSolutionInputBuilder

Creates a new builder-style object to manufacture CreateSolutionInput.

Trait Implementations

impl Clone for CreateSolutionInput

fn clone(&self) -> CreateSolutionInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateSolutionInput

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

Formats the value using the given formatter.

impl PartialEq for CreateSolutionInput

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