Struct aws_sdk_personalize::operation::create_solution::builders::CreateSolutionInputBuilder

#[non_exhaustive]
pub struct CreateSolutionInputBuilder { /* private fields */ }

A builder for CreateSolutionInput.

Implementations

impl CreateSolutionInputBuilder

pub fn name(self, input: impl Into<String>) -> Self

The name for the solution.

This field is required.

pub fn set_name(self, input: Option<String>) -> Self

The name for the solution.

pub fn get_name(&self) -> &Option<String>

The name for the solution.

pub fn perform_hpo(self, input: bool) -> Self

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 set_perform_hpo(self, input: Option<bool>) -> Self

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 get_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, input: bool) -> Self

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 set_perform_auto_ml(self, input: Option<bool>) -> Self

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 get_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, input: impl Into<String>) -> Self

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

pub fn set_recipe_arn(self, input: Option<String>) -> Self

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

pub fn get_recipe_arn(&self) -> &Option<String>

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

pub fn dataset_group_arn(self, input: impl Into<String>) -> Self

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

This field is required.

pub fn set_dataset_group_arn(self, input: Option<String>) -> Self

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

pub fn get_dataset_group_arn(&self) -> &Option<String>

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

pub fn event_type(self, input: impl Into<String>) -> Self

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 set_event_type(self, input: Option<String>) -> Self

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 get_event_type(&self) -> &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.

pub fn solution_config(self, input: SolutionConfig) -> Self

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 set_solution_config(self, input: Option<SolutionConfig>) -> Self

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 get_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, input: Tag) -> Self

Appends an item to tags.

To override the contents of this collection use set_tags.

A list of tags to apply to the solution.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

A list of tags to apply to the solution.

pub fn get_tags(&self) -> &Option<Vec<Tag>>

A list of tags to apply to the solution.

pub fn build(self) -> Result<CreateSolutionInput, BuildError>

Consumes the builder and constructs a CreateSolutionInput.

impl CreateSolutionInputBuilder

pub async fn send_with( self, client: &Client ) -> Result<CreateSolutionOutput, SdkError<CreateSolutionError, HttpResponse>>

Sends a request with this input using the given client.

Trait Implementations

impl Clone for CreateSolutionInputBuilder

fn clone(&self) -> CreateSolutionInputBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateSolutionInputBuilder

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

Formats the value using the given formatter.

impl Default for CreateSolutionInputBuilder

fn default() -> CreateSolutionInputBuilder

Returns the “default value” for a type.

impl PartialEq for CreateSolutionInputBuilder

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