Struct aws_sdk_machinelearning::model::Evaluation

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

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

Implementations

impl Evaluation

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

The ID that is assigned to the Evaluation at creation.

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

The ID of the MLModel that is the focus of the evaluation.

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

The ID of the DataSource that is used to evaluate the MLModel.

pub fn input_data_location_s3(&self) -> Option<&str>

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

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

The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

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

The time that the Evaluation was created. The time is expressed in epoch time.

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

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

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

A user-supplied name or description of the Evaluation.

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

The status of the evaluation. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel.

• INPROGRESS - The evaluation is underway.

• FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.

• COMPLETED - The evaluation process completed successfully.

• DELETED - The Evaluation is marked as deleted. It is not usable.

pub fn performance_metrics(&self) -> Option<&PerformanceMetrics>

Measurements of how well the MLModel performed, using observations referenced by the DataSource. One of the following metrics is returned, based on the type of the MLModel:

• BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.

• RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.

• MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

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

A description of the most recent details about evaluating the MLModel.

pub fn compute_time(&self) -> Option<i64>

Long integer type that is a 64-bit signed number.

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

A timestamp represented in epoch time.

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

A timestamp represented in epoch time.

impl Evaluation

pub fn builder() -> Builder

Creates a new builder-style object to manufacture Evaluation.

Trait Implementations

impl Clone for Evaluation

fn clone(&self) -> Evaluation

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Evaluation

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

Formats the value using the given formatter.

impl PartialEq<Evaluation> for Evaluation

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