Struct aws_sdk_machinelearning::types::builders::EvaluationBuilder

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

A builder for Evaluation.

Implementations

impl EvaluationBuilder

pub fn evaluation_id(self, input: impl Into<String>) -> Self

The ID that is assigned to the Evaluation at creation.

pub fn set_evaluation_id(self, input: Option<String>) -> Self

The ID that is assigned to the Evaluation at creation.

pub fn get_evaluation_id(&self) -> &Option<String>

The ID that is assigned to the Evaluation at creation.

pub fn ml_model_id(self, input: impl Into<String>) -> Self

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

pub fn set_ml_model_id(self, input: Option<String>) -> Self

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

pub fn get_ml_model_id(&self) -> &Option<String>

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

pub fn evaluation_data_source_id(self, input: impl Into<String>) -> Self

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

pub fn set_evaluation_data_source_id(self, input: Option<String>) -> Self

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

pub fn get_evaluation_data_source_id(&self) -> &Option<String>

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

pub fn input_data_location_s3(self, input: impl Into<String>) -> Self

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

pub fn set_input_data_location_s3(self, input: Option<String>) -> Self

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

pub fn get_input_data_location_s3(&self) -> &Option<String>

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, input: impl Into<String>) -> Self

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 set_created_by_iam_user(self, input: Option<String>) -> Self

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 get_created_by_iam_user(&self) -> &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.

pub fn created_at(self, input: DateTime) -> Self

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

pub fn set_created_at(self, input: Option<DateTime>) -> Self

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

pub fn get_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, input: DateTime) -> Self

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

pub fn set_last_updated_at(self, input: Option<DateTime>) -> Self

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

pub fn get_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, input: impl Into<String>) -> Self

A user-supplied name or description of the Evaluation.

pub fn set_name(self, input: Option<String>) -> Self

A user-supplied name or description of the Evaluation.

pub fn get_name(&self) -> &Option<String>

A user-supplied name or description of the Evaluation.

pub fn status(self, input: EntityStatus) -> Self

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 set_status(self, input: Option<EntityStatus>) -> Self

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 get_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, input: PerformanceMetrics) -> Self

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 set_performance_metrics(self, input: Option<PerformanceMetrics>) -> Self

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 get_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, input: impl Into<String>) -> Self

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

pub fn set_message(self, input: Option<String>) -> Self

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

pub fn get_message(&self) -> &Option<String>

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

pub fn compute_time(self, input: i64) -> Self

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

pub fn set_compute_time(self, input: Option<i64>) -> Self

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

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

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

pub fn finished_at(self, input: DateTime) -> Self

A timestamp represented in epoch time.

pub fn set_finished_at(self, input: Option<DateTime>) -> Self

A timestamp represented in epoch time.

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

A timestamp represented in epoch time.

pub fn started_at(self, input: DateTime) -> Self

A timestamp represented in epoch time.

pub fn set_started_at(self, input: Option<DateTime>) -> Self

A timestamp represented in epoch time.

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

A timestamp represented in epoch time.

pub fn build(self) -> Evaluation

Consumes the builder and constructs a Evaluation.

Trait Implementations

impl Clone for EvaluationBuilder

fn clone(&self) -> EvaluationBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for EvaluationBuilder

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

Formats the value using the given formatter.

impl Default for EvaluationBuilder

fn default() -> EvaluationBuilder

Returns the “default value” for a type.

impl PartialEq for EvaluationBuilder

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