Struct rusoto_forecast::CreatePredictorRequest

pub struct CreatePredictorRequest {
    pub algorithm_arn: Option<String>,
    pub encryption_config: Option<EncryptionConfig>,
    pub evaluation_parameters: Option<EvaluationParameters>,
    pub featurization_config: FeaturizationConfig,
    pub forecast_horizon: i64,
    pub hpo_config: Option<HyperParameterTuningJobConfig>,
    pub input_data_config: InputDataConfig,
    pub perform_auto_ml: Option<bool>,
    pub perform_hpo: Option<bool>,
    pub predictor_name: String,
    pub tags: Option<Vec<Tag>>,
    pub training_parameters: Option<HashMap<String, String>>,
}

Fields

algorithm_arn: Option<String>

The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true.

Supported algorithms:

• arn:aws:forecast:::algorithm/ARIMA

• arn:aws:forecast:::algorithm/DeepARPlus

  Supports hyperparameter optimization (HPO)

• arn:aws:forecast:::algorithm/ETS

• arn:aws:forecast:::algorithm/NPTS

• arn:aws:forecast:::algorithm/Prophet

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.

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: FeaturizationConfig

The featurization configuration.

forecast_horizon: i64

Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.

For example, if you configure a dataset for daily data collection (using the DataFrequency parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.

The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.

hpo_config: Option<HyperParameterTuningJobConfig>

Provides hyperparameter override values for the algorithm. If you don't provide this parameter, Amazon Forecast uses default values. The individual algorithms specify which hyperparameters support hyperparameter optimization (HPO). For more information, see aws-forecast-choosing-recipes.

If you included the HPOConfig object, you must set PerformHPO to true.

input_data_config: InputDataConfig

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

perform_auto_ml: Option<bool>

Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.

The default value is false. In this case, you are required to specify an algorithm.

Set PerformAutoML to true to have Amazon Forecast perform AutoML. This is a good option if you aren't sure which algorithm is suitable for your training data. In this case, PerformHPO must be false.

perform_hpo: Option<bool>

Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.

The default value is false. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.

To override the default values, set PerformHPO to true and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML must be false.

The following algorithm supports HPO:

• DeepAR+

predictor_name: String

A name for the predictor.

tags: Option<Vec<Tag>>

The optional metadata that you apply to the predictor to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

• Maximum number of tags per resource - 50.

• For each resource, each tag key must be unique, and each tag key can have only one value.

• Maximum key length - 128 Unicode characters in UTF-8.

• Maximum value length - 256 Unicode characters in UTF-8.

• If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

• Tag keys and values are case sensitive.

• Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

training_parameters: Option<HashMap<String, String>>

The hyperparameters to override for model training. The hyperparameters that you can override are listed in the individual algorithms. For the list of supported algorithms, see aws-forecast-choosing-recipes.

Trait Implementations

impl Clone for CreatePredictorRequest

pub fn clone(&self) -> CreatePredictorRequest

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreatePredictorRequest

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

Formats the value using the given formatter.

impl Default for CreatePredictorRequest

pub fn default() -> CreatePredictorRequest

Returns the "default value" for a type.

impl PartialEq<CreatePredictorRequest> for CreatePredictorRequest

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

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

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

This method tests for !=.

impl Serialize for CreatePredictorRequest

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CreatePredictorRequest