Struct aws_sdk_personalize::types::Solution

#[non_exhaustive]
pub struct Solution {

    pub name: Option<String>,
    pub solution_arn: Option<String>,
    pub perform_hpo: bool,
    pub perform_auto_ml: bool,
    pub recipe_arn: Option<String>,
    pub dataset_group_arn: Option<String>,
    pub event_type: Option<String>,
    pub solution_config: Option<SolutionConfig>,
    pub auto_ml_result: Option<AutoMlResult>,
    pub status: Option<String>,
    pub creation_date_time: Option<DateTime>,
    pub last_updated_date_time: Option<DateTime>,
    pub latest_solution_version: Option<SolutionVersionSummary>,
}

An object that provides information about a solution. A solution is a trained model that can be deployed as a campaign.

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 of the solution.

solution_arn: Option<String>

The ARN of the solution.

perform_hpo: bool

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

perform_auto_ml: 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.

When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (recipeArn must not be specified). When false (the default), Amazon Personalize uses recipeArn for training.

recipe_arn: Option<String>

The ARN of the recipe used to create the solution. 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>

The event type (for example, 'click' or 'like') that is used for training the model. If no eventType is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.

solution_config: Option<SolutionConfig>

Describes the configuration properties for the solution.

auto_ml_result: Option<AutoMlResult>

When performAutoML is true, specifies the best recipe found.

status: Option<String>

The status of the solution.

A solution can be in one of the following states:

• CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

• DELETE PENDING > DELETE IN_PROGRESS

creation_date_time: Option<DateTime>

The creation date and time (in Unix time) of the solution.

last_updated_date_time: Option<DateTime>

The date and time (in Unix time) that the solution was last updated.

latest_solution_version: Option<SolutionVersionSummary>

Describes the latest version of the solution, including the status and the ARN.

Implementations

impl Solution

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

The name of the solution.

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

The ARN of the solution.

pub fn perform_hpo(&self) -> bool

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

pub fn perform_auto_ml(&self) -> 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.

When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration (recipeArn must not be specified). When false (the default), Amazon Personalize uses recipeArn for training.

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

The ARN of the recipe used to create the solution. 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>

The event type (for example, 'click' or 'like') that is used for training the model. If no eventType is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.

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

Describes the configuration properties for the solution.

pub fn auto_ml_result(&self) -> Option<&AutoMlResult>

When performAutoML is true, specifies the best recipe found.

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

The status of the solution.

A solution can be in one of the following states:

• CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

• DELETE PENDING > DELETE IN_PROGRESS

pub fn creation_date_time(&self) -> Option<&DateTime>

The creation date and time (in Unix time) of the solution.

pub fn last_updated_date_time(&self) -> Option<&DateTime>

The date and time (in Unix time) that the solution was last updated.

pub fn latest_solution_version(&self) -> Option<&SolutionVersionSummary>

Describes the latest version of the solution, including the status and the ARN.

impl Solution

pub fn builder() -> SolutionBuilder

Creates a new builder-style object to manufacture Solution.

Trait Implementations

impl Clone for Solution

fn clone(&self) -> Solution

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Solution

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

Formats the value using the given formatter.

impl PartialEq for Solution

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