Struct aws_sdk_machinelearning::output::GetEvaluationOutput

#[non_exhaustive]
pub struct GetEvaluationOutput {

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

Represents the output of a GetEvaluation operation and describes an Evaluation.

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.

evaluation_id: Option<String>

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

ml_model_id: Option<String>

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

evaluation_data_source_id: Option<String>

The DataSource used for this evaluation.

input_data_location_s3: Option<String>

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

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.

created_at: Option<DateTime>

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

last_updated_at: Option<DateTime>

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

name: Option<String>

A user-supplied name or description of the Evaluation.

status: Option<EntityStatus>

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.

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.

log_uri: Option<String>

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

message: Option<String>

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

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.

finished_at: Option<DateTime>

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.

started_at: Option<DateTime>

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

Implementations

impl GetEvaluationOutput

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

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

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

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

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

The DataSource used for this evaluation.

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

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

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 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.

pub fn performance_metrics(&self) -> 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.

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

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

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

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

pub fn compute_time(&self) -> 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.

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

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.

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

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

impl GetEvaluationOutput

pub fn builder() -> Builder

Creates a new builder-style object to manufacture GetEvaluationOutput

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 PartialEq<GetEvaluationOutput> for GetEvaluationOutput

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl StructuralPartialEq for GetEvaluationOutput