Struct aws_sdk_forecast::types::FeaturizationConfig

#[non_exhaustive]
pub struct FeaturizationConfig {
    pub forecast_frequency: String,
    pub forecast_dimensions: Option<Vec<String>>,
    pub featurizations: Option<Vec<Featurization>>,
}

This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AttributeConfig.

In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.

You define featurization using the FeaturizationConfig object. You specify an array of transformations, one for each field that you want to featurize. You then include the FeaturizationConfig object in your CreatePredictor request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training.

You can create multiple featurization configurations. For example, you might call the CreatePredictor operation twice by specifying different featurization configurations.

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.

forecast_frequency: String

The frequency of predictions in a forecast.

Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:

• Minute - 1-59

• Hour - 1-23

• Day - 1-6

• Week - 1-4

• Month - 1-11

• Year - 1

Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".

The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.

forecast_dimensions: Option<Vec<String>>

An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

featurizations: Option<Vec<Featurization>>

An array of featurization (transformation) information for the fields of a dataset.

Implementations

impl FeaturizationConfig

pub fn forecast_frequency(&self) -> &str

The frequency of predictions in a forecast.

Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:

• Minute - 1-59

• Hour - 1-23

• Day - 1-6

• Week - 1-4

• Month - 1-11

• Year - 1

Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".

The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.

When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.

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

An array of dimension (field) names that specify how to group the generated forecast.

For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.

All forecast dimensions specified in the TARGET_TIME_SERIES dataset don't need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.

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 featurizations(&self) -> &[Featurization]

An array of featurization (transformation) information for the fields of a dataset.

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

impl FeaturizationConfig

pub fn builder() -> FeaturizationConfigBuilder

Creates a new builder-style object to manufacture FeaturizationConfig.

Trait Implementations

impl Clone for FeaturizationConfig

fn clone(&self) -> FeaturizationConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for FeaturizationConfig

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

Formats the value using the given formatter.

impl PartialEq for FeaturizationConfig

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