Struct aws_sdk_forecast::operation::describe_auto_predictor::DescribeAutoPredictorOutput

#[non_exhaustive]
pub struct DescribeAutoPredictorOutput {

    pub predictor_arn: Option<String>,
    pub predictor_name: Option<String>,
    pub forecast_horizon: Option<i32>,
    pub forecast_types: Option<Vec<String>>,
    pub forecast_frequency: Option<String>,
    pub forecast_dimensions: Option<Vec<String>>,
    pub dataset_import_job_arns: Option<Vec<String>>,
    pub data_config: Option<DataConfig>,
    pub encryption_config: Option<EncryptionConfig>,
    pub reference_predictor_summary: Option<ReferencePredictorSummary>,
    pub estimated_time_remaining_in_minutes: Option<i64>,
    pub status: Option<String>,
    pub message: Option<String>,
    pub creation_time: Option<DateTime>,
    pub last_modification_time: Option<DateTime>,
    pub optimization_metric: Option<OptimizationMetric>,
    pub explainability_info: Option<ExplainabilityInfo>,
    pub monitor_info: Option<MonitorInfo>,
    pub time_alignment_boundary: Option<TimeAlignmentBoundary>,
    /* private fields */
}

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.

predictor_arn: Option<String>

The Amazon Resource Name (ARN) of the predictor

predictor_name: Option<String>

The name of the predictor.

forecast_horizon: Option<i32>

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

forecast_types: Option<Vec<String>>

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

forecast_frequency: 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.

forecast_dimensions: Option<Vec<String>>

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

dataset_import_job_arns: Option<Vec<String>>

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

data_config: Option<DataConfig>

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

encryption_config: 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.

reference_predictor_summary: Option<ReferencePredictorSummary>

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

estimated_time_remaining_in_minutes: Option<i64>

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

status: 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

message: Option<String>

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

creation_time: Option<DateTime>

The timestamp of the CreateAutoPredictor request.

last_modification_time: 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.

optimization_metric: Option<OptimizationMetric>

The accuracy metric used to optimize the predictor.

explainability_info: Option<ExplainabilityInfo>

Provides the status and ARN of the Predictor Explainability.

monitor_info: Option<MonitorInfo>

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

time_alignment_boundary: Option<TimeAlignmentBoundary>

The time boundary Forecast uses when aggregating data.

Implementations

impl DescribeAutoPredictorOutput

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

The Amazon Resource Name (ARN) of the predictor

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

The name of the predictor.

pub fn 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) -> &[String]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .forecast_types.is_none().

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

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) -> &[String]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .forecast_dimensions.is_none().

pub fn dataset_import_job_arns(&self) -> &[String]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .dataset_import_job_arns.is_none().

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

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

pub fn 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) -> 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) -> Option<i64>

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

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

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) -> Option<&str>

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

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

The timestamp of the CreateAutoPredictor request.

pub fn 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) -> Option<&OptimizationMetric>

The accuracy metric used to optimize the predictor.

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

Provides the status and ARN of the Predictor Explainability.

pub fn 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) -> Option<&TimeAlignmentBoundary>

The time boundary Forecast uses when aggregating data.

impl DescribeAutoPredictorOutput

pub fn builder() -> DescribeAutoPredictorOutputBuilder

Creates a new builder-style object to manufacture DescribeAutoPredictorOutput.

Trait Implementations

impl Clone for DescribeAutoPredictorOutput

fn clone(&self) -> DescribeAutoPredictorOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribeAutoPredictorOutput

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

Formats the value using the given formatter.

impl PartialEq for DescribeAutoPredictorOutput

fn eq(&self, other: &DescribeAutoPredictorOutput) -> 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 RequestId for DescribeAutoPredictorOutput

fn request_id(&self) -> Option<&str>

Returns the request ID, or None if the service could not be reached.

impl StructuralPartialEq for DescribeAutoPredictorOutput