Struct aws_sdk_forecast::operation::describe_predictor::DescribePredictorOutput

#[non_exhaustive]
pub struct DescribePredictorOutput {

    pub predictor_arn: Option<String>,
    pub predictor_name: Option<String>,
    pub algorithm_arn: Option<String>,
    pub auto_ml_algorithm_arns: Option<Vec<String>>,
    pub forecast_horizon: Option<i32>,
    pub forecast_types: Option<Vec<String>>,
    pub perform_auto_ml: Option<bool>,
    pub auto_ml_override_strategy: Option<AutoMlOverrideStrategy>,
    pub perform_hpo: Option<bool>,
    pub training_parameters: Option<HashMap<String, String>>,
    pub evaluation_parameters: Option<EvaluationParameters>,
    pub hpo_config: Option<HyperParameterTuningJobConfig>,
    pub input_data_config: Option<InputDataConfig>,
    pub featurization_config: Option<FeaturizationConfig>,
    pub encryption_config: Option<EncryptionConfig>,
    pub predictor_execution_details: Option<PredictorExecutionDetails>,
    pub estimated_time_remaining_in_minutes: Option<i64>,
    pub is_auto_predictor: Option<bool>,
    pub dataset_import_job_arns: Option<Vec<String>>,
    pub status: Option<String>,
    pub message: Option<String>,
    pub creation_time: Option<DateTime>,
    pub last_modification_time: Option<DateTime>,
    pub optimization_metric: Option<OptimizationMetric>,
    /* 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 ARN of the predictor.

predictor_name: Option<String>

The name of the predictor.

algorithm_arn: Option<String>

The Amazon Resource Name (ARN) of the algorithm used for model training.

auto_ml_algorithm_arns: Option<Vec<String>>

When PerformAutoML is specified, the ARN of the chosen algorithm.

forecast_horizon: Option<i32>

The number of time-steps of the forecast. 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"]

perform_auto_ml: Option<bool>

Whether the predictor is set to perform AutoML.

auto_ml_override_strategy: Option<AutoMlOverrideStrategy>

The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.

The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

This parameter is only valid for predictors trained using AutoML.

perform_hpo: Option<bool>

Whether the predictor is set to perform hyperparameter optimization (HPO).

training_parameters: Option<HashMap<String, String>>

The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.

evaluation_parameters: Option<EvaluationParameters>

Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

hpo_config: Option<HyperParameterTuningJobConfig>

The hyperparameter override values for the algorithm.

input_data_config: Option<InputDataConfig>

Describes the dataset group that contains the data to use to train the predictor.

featurization_config: Option<FeaturizationConfig>

The featurization configuration.

encryption_config: Option<EncryptionConfig>

An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

predictor_execution_details: Option<PredictorExecutionDetails>

Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.

estimated_time_remaining_in_minutes: Option<i64>

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

is_auto_predictor: Option<bool>

Whether the predictor was created with CreateAutoPredictor.

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.

status: Option<String>

The status of the predictor. States include:

• ACTIVE

• CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

• DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

• CREATE_STOPPING, CREATE_STOPPED

The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.

message: Option<String>

If an error occurred, an informational message about the error.

creation_time: Option<DateTime>

When the model training task was created.

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.

Implementations

impl DescribePredictorOutput

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

The ARN of the predictor.

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

The name of the predictor.

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

The Amazon Resource Name (ARN) of the algorithm used for model training.

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

When PerformAutoML is specified, the ARN of the chosen algorithm.

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

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

The number of time-steps of the forecast. 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 perform_auto_ml(&self) -> Option<bool>

Whether the predictor is set to perform AutoML.

pub fn auto_ml_override_strategy(&self) -> Option<&AutoMlOverrideStrategy>

The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.

The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

This parameter is only valid for predictors trained using AutoML.

pub fn perform_hpo(&self) -> Option<bool>

Whether the predictor is set to perform hyperparameter optimization (HPO).

pub fn training_parameters(&self) -> Option<&HashMap<String, String>>

The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.

pub fn evaluation_parameters(&self) -> Option<&EvaluationParameters>

Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

pub fn hpo_config(&self) -> Option<&HyperParameterTuningJobConfig>

The hyperparameter override values for the algorithm.

pub fn input_data_config(&self) -> Option<&InputDataConfig>

Describes the dataset group that contains the data to use to train the predictor.

pub fn featurization_config(&self) -> Option<&FeaturizationConfig>

The featurization configuration.

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

An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

pub fn predictor_execution_details(&self) -> Option<&PredictorExecutionDetails>

Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.

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 is_auto_predictor(&self) -> Option<bool>

Whether the predictor was created with CreateAutoPredictor.

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

The status of the predictor. States include:

• ACTIVE

• CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

• DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

• CREATE_STOPPING, CREATE_STOPPED

The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.

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

If an error occurred, an informational message about the error.

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

When the model training task was created.

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.

impl DescribePredictorOutput

pub fn builder() -> DescribePredictorOutputBuilder

Creates a new builder-style object to manufacture DescribePredictorOutput.

Trait Implementations

impl Clone for DescribePredictorOutput

fn clone(&self) -> DescribePredictorOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribePredictorOutput

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

Formats the value using the given formatter.

impl PartialEq for DescribePredictorOutput

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

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

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

impl StructuralPartialEq for DescribePredictorOutput