Struct aws_sdk_machinelearning::operation::get_evaluation::builders::GetEvaluationOutputBuilder

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

A builder for GetEvaluationOutput.

Implementations

impl GetEvaluationOutputBuilder

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

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

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

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

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

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

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

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

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

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

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

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

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

The DataSource used for this evaluation.

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

The DataSource used for this evaluation.

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

The DataSource used for this evaluation.

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

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

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

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

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

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

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

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 get_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, input: PerformanceMetrics) -> Self

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

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

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

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

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

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

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

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

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 get_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, input: DateTime) -> Self

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

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 get_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, input: DateTime) -> Self

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

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

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

pub fn get_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.

pub fn build(self) -> GetEvaluationOutput

Consumes the builder and constructs a GetEvaluationOutput.

Trait Implementations

impl Clone for GetEvaluationOutputBuilder

fn clone(&self) -> GetEvaluationOutputBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for GetEvaluationOutputBuilder

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

Formats the value using the given formatter.

impl Default for GetEvaluationOutputBuilder

fn default() -> GetEvaluationOutputBuilder

Returns the “default value” for a type.

impl PartialEq for GetEvaluationOutputBuilder

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