Struct rusoto_forecast::DescribePredictorResponse

pub struct DescribePredictorResponse {

    pub algorithm_arn: Option<String>,
    pub auto_ml_algorithm_arns: Option<Vec<String>>,
    pub auto_ml_override_strategy: Option<String>,
    pub creation_time: Option<f64>,
    pub dataset_import_job_arns: Option<Vec<String>>,
    pub encryption_config: Option<EncryptionConfig>,
    pub estimated_time_remaining_in_minutes: Option<i64>,
    pub evaluation_parameters: Option<EvaluationParameters>,
    pub featurization_config: Option<FeaturizationConfig>,
    pub forecast_horizon: Option<i64>,
    pub forecast_types: Option<Vec<String>>,
    pub hpo_config: Option<HyperParameterTuningJobConfig>,
    pub input_data_config: Option<InputDataConfig>,
    pub last_modification_time: Option<f64>,
    pub message: Option<String>,
    pub perform_auto_ml: Option<bool>,
    pub perform_hpo: Option<bool>,
    pub predictor_arn: Option<String>,
    pub predictor_execution_details: Option<PredictorExecutionDetails>,
    pub predictor_name: Option<String>,
    pub status: Option<String>,
    pub training_parameters: Option<HashMap<String, String>>,
}

Fields

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.

auto_ml_override_strategy: Option<String>

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.

creation_time: Option<f64>

When the model training task was created.

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.

encryption_config: Option<EncryptionConfig>

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

estimated_time_remaining_in_minutes: Option<i64>

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

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.

featurization_config: Option<FeaturizationConfig>

The featurization configuration.

forecast_horizon: Option<i64>

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"]

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.

last_modification_time: Option<f64>

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

• CREATEPENDING - The CreationTime.

• CREATEINPROGRESS - The current timestamp.

• CREATESTOPPING - The current timestamp.

• CREATESTOPPED - When the job stopped.

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

message: Option<String>

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

perform_auto_ml: Option<bool>

Whether the predictor is set to perform AutoML.

perform_hpo: Option<bool>

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

predictor_arn: Option<String>

The ARN of the predictor.

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.

predictor_name: Option<String>

The name of the predictor.

status: Option<String>

The status of the predictor. States include:

• ACTIVE

• CREATEPENDING, CREATEINPROGRESS, CREATEFAILED

• DELETEPENDING, DELETEINPROGRESS, DELETEFAILED

• CREATESTOPPING, CREATESTOPPED

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

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.

Trait Implementations

impl Clone for DescribePredictorResponse

fn clone(&self) -> DescribePredictorResponse

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribePredictorResponse

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

Formats the value using the given formatter.

impl Default for DescribePredictorResponse

fn default() -> DescribePredictorResponse

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for DescribePredictorResponse

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl PartialEq<DescribePredictorResponse> for DescribePredictorResponse

fn eq(&self, other: &DescribePredictorResponse) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &DescribePredictorResponse) -> bool

This method tests for !=.

impl StructuralPartialEq for DescribePredictorResponse