Struct Evaluation

pub struct Evaluation {

    pub compute_time: Option<i64>,
    pub created_at: Option<f64>,
    pub created_by_iam_user: Option<String>,
    pub evaluation_data_source_id: Option<String>,
    pub evaluation_id: Option<String>,
    pub finished_at: Option<f64>,
    pub input_data_location_s3: Option<String>,
    pub last_updated_at: Option<f64>,
    pub ml_model_id: Option<String>,
    pub message: Option<String>,
    pub name: Option<String>,
    pub performance_metrics: Option<PerformanceMetrics>,
    pub started_at: Option<f64>,
    pub status: Option<String>,
}

Represents the output of GetEvaluation operation.

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

Fields

compute_time: Option<i64>

created_at: Option<f64>

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

created_by_iam_user: Option<String>

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.

evaluation_data_source_id: Option<String>

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

evaluation_id: Option<String>

The ID that is assigned to the Evaluation at creation.

finished_at: Option<f64>

input_data_location_s3: Option<String>

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

last_updated_at: Option<f64>

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

ml_model_id: Option<String>

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

message: Option<String>

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

name: Option<String>

A user-supplied name or description of the Evaluation.

performance_metrics: 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.

started_at: Option<f64>

status: Option<String>

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.

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 Default for Evaluation

fn default() -> Evaluation

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Evaluation

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for Evaluation

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

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for Evaluation