Struct aws_sdk_forecast::operation::describe_auto_predictor::builders::DescribeAutoPredictorOutputBuilder

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

A builder for DescribeAutoPredictorOutput.

Implementations

impl DescribeAutoPredictorOutputBuilder

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

The Amazon Resource Name (ARN) of the predictor

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

The Amazon Resource Name (ARN) of the predictor

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

The Amazon Resource Name (ARN) of the predictor

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

The name of the predictor.

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

The name of the predictor.

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

The name of the predictor.

pub fn forecast_horizon(self, input: i32) -> Self

The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.

pub fn set_forecast_horizon(self, input: Option<i32>) -> Self

The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.

pub fn get_forecast_horizon(&self) -> &Option<i32>

The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.

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

Appends an item to forecast_types.

To override the contents of this collection use set_forecast_types.

The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"].

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

The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"].

pub fn get_forecast_types(&self) -> &Option<Vec<String>>

The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"].

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

The frequency of predictions in a forecast.

Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.

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

The frequency of predictions in a forecast.

Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.

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

The frequency of predictions in a forecast.

Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.

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

Appends an item to forecast_dimensions.

To override the contents of this collection use set_forecast_dimensions.

An array of dimension (field) names that specify the attributes used to group your time series.

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

An array of dimension (field) names that specify the attributes used to group your time series.

pub fn get_forecast_dimensions(&self) -> &Option<Vec<String>>

An array of dimension (field) names that specify the attributes used to group your time series.

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

Appends an item to dataset_import_job_arns.

To override the contents of this collection use set_dataset_import_job_arns.

An array of the ARNs of the dataset import jobs used to import training data for the predictor.

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

An array of the ARNs of the dataset import jobs used to import training data for the predictor.

pub fn get_dataset_import_job_arns(&self) -> &Option<Vec<String>>

An array of the ARNs of the dataset import jobs used to import training data for the predictor.

pub fn data_config(self, input: DataConfig) -> Self

The data configuration for your dataset group and any additional datasets.

pub fn set_data_config(self, input: Option<DataConfig>) -> Self

The data configuration for your dataset group and any additional datasets.

pub fn get_data_config(&self) -> &Option<DataConfig>

The data configuration for your dataset group and any additional datasets.

pub fn encryption_config(self, input: EncryptionConfig) -> Self

An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.

pub fn set_encryption_config(self, input: Option<EncryptionConfig>) -> Self

An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.

pub fn get_encryption_config(&self) -> &Option<EncryptionConfig>

An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.

pub fn reference_predictor_summary( self, input: ReferencePredictorSummary ) -> Self

The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.

pub fn set_reference_predictor_summary( self, input: Option<ReferencePredictorSummary> ) -> Self

The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.

pub fn get_reference_predictor_summary( &self ) -> &Option<ReferencePredictorSummary>

The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.

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

The estimated time remaining in minutes for the predictor training job to complete.

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

The estimated time remaining in minutes for the predictor training job to complete.

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

The estimated time remaining in minutes for the predictor training job to complete.

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

The status of the predictor. States include:

• ACTIVE

• CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

• CREATE_STOPPING, CREATE_STOPPED

• DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

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

The status of the predictor. States include:

• ACTIVE

• CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

• CREATE_STOPPING, CREATE_STOPPED

• DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

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

The status of the predictor. States include:

• ACTIVE

• CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

• CREATE_STOPPING, CREATE_STOPPED

• DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

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

In the event of an error, a message detailing the cause of the error.

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

In the event of an error, a message detailing the cause of the error.

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

In the event of an error, a message detailing the cause of the error.

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

The timestamp of the CreateAutoPredictor request.

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

The timestamp of the CreateAutoPredictor request.

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

The timestamp of the CreateAutoPredictor request.

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

The last time the resource was modified. The timestamp depends on the status of the job:

• CREATE_PENDING - The CreationTime.

• CREATE_IN_PROGRESS - The current timestamp.

• CREATE_STOPPING - The current timestamp.

• CREATE_STOPPED - When the job stopped.

• ACTIVE or CREATE_FAILED - When the job finished or failed.

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

The last time the resource was modified. The timestamp depends on the status of the job:

• CREATE_PENDING - The CreationTime.

• CREATE_IN_PROGRESS - The current timestamp.

• CREATE_STOPPING - The current timestamp.

• CREATE_STOPPED - When the job stopped.

• ACTIVE or CREATE_FAILED - When the job finished or failed.

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

The last time the resource was modified. The timestamp depends on the status of the job:

• CREATE_PENDING - The CreationTime.

• CREATE_IN_PROGRESS - The current timestamp.

• CREATE_STOPPING - The current timestamp.

• CREATE_STOPPED - When the job stopped.

• ACTIVE or CREATE_FAILED - When the job finished or failed.

pub fn optimization_metric(self, input: OptimizationMetric) -> Self

The accuracy metric used to optimize the predictor.

pub fn set_optimization_metric(self, input: Option<OptimizationMetric>) -> Self

The accuracy metric used to optimize the predictor.

pub fn get_optimization_metric(&self) -> &Option<OptimizationMetric>

The accuracy metric used to optimize the predictor.

pub fn explainability_info(self, input: ExplainabilityInfo) -> Self

Provides the status and ARN of the Predictor Explainability.

pub fn set_explainability_info(self, input: Option<ExplainabilityInfo>) -> Self

Provides the status and ARN of the Predictor Explainability.

pub fn get_explainability_info(&self) -> &Option<ExplainabilityInfo>

Provides the status and ARN of the Predictor Explainability.

pub fn monitor_info(self, input: MonitorInfo) -> Self

A object with the Amazon Resource Name (ARN) and status of the monitor resource.

pub fn set_monitor_info(self, input: Option<MonitorInfo>) -> Self

A object with the Amazon Resource Name (ARN) and status of the monitor resource.

pub fn get_monitor_info(&self) -> &Option<MonitorInfo>

A object with the Amazon Resource Name (ARN) and status of the monitor resource.

pub fn time_alignment_boundary(self, input: TimeAlignmentBoundary) -> Self

The time boundary Forecast uses when aggregating data.

pub fn set_time_alignment_boundary( self, input: Option<TimeAlignmentBoundary> ) -> Self

The time boundary Forecast uses when aggregating data.

pub fn get_time_alignment_boundary(&self) -> &Option<TimeAlignmentBoundary>

The time boundary Forecast uses when aggregating data.

pub fn build(self) -> DescribeAutoPredictorOutput

Consumes the builder and constructs a DescribeAutoPredictorOutput.

Trait Implementations

impl Clone for DescribeAutoPredictorOutputBuilder

fn clone(&self) -> DescribeAutoPredictorOutputBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribeAutoPredictorOutputBuilder

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

Formats the value using the given formatter.

impl Default for DescribeAutoPredictorOutputBuilder

fn default() -> DescribeAutoPredictorOutputBuilder

Returns the “default value” for a type.

impl PartialEq for DescribeAutoPredictorOutputBuilder

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