Struct GetEvaluationOutput

pub struct GetEvaluationOutput {

    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 log_uri: Option<String>,
    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 a GetEvaluation operation and describes an Evaluation.

Fields

compute_time: Option<i64>

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation, normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state.

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 DataSource used for this evaluation.

evaluation_id: Option<String>

The evaluation ID which is same as the EvaluationId in the request.

finished_at: Option<f64>

The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state.

input_data_location_s3: Option<String>

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

last_updated_at: Option<f64>

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

log_uri: Option<String>

A link to the file that contains logs of the CreateEvaluation operation.

ml_model_id: Option<String>

The ID of the MLModel that was 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 metric 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>

The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state.

status: Option<String>

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

• PENDING - Amazon Machine Language (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 GetEvaluationOutput

fn clone(&self) -> GetEvaluationOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for GetEvaluationOutput

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

Formats the value using the given formatter.

impl Default for GetEvaluationOutput

fn default() -> GetEvaluationOutput

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GetEvaluationOutput

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for GetEvaluationOutput

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