Struct aws_sdk_forecast::client::fluent_builders::CreatePredictor

pub struct CreatePredictor { /* private fields */ }

Fluent builder constructing a request to CreatePredictor.

This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast. To create a predictor that is compatible with all aspects of Forecast, use CreateAutoPredictor.

Creates an Amazon Forecast predictor.

In the request, provide a dataset group and either specify an algorithm or let Amazon Forecast choose an algorithm for you using AutoML. If you specify an algorithm, you also can override algorithm-specific hyperparameters.

Amazon Forecast uses the algorithm to train a predictor using the latest version of the datasets in the specified dataset group. You can then generate a forecast using the CreateForecast operation.

To see the evaluation metrics, use the GetAccuracyMetrics operation.

You can specify a featurization configuration to fill and aggregate the data fields in the TARGET_TIME_SERIES dataset to improve model training. For more information, see FeaturizationConfig.

For RELATED_TIME_SERIES datasets, CreatePredictor verifies that the DataFrequency specified when the dataset was created matches the ForecastFrequency. TARGET_TIME_SERIES datasets don't have this restriction. Amazon Forecast also verifies the delimiter and timestamp format. For more information, see howitworks-datasets-groups.

By default, predictors are trained and evaluated at the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles. You can choose custom forecast types to train and evaluate your predictor by setting the ForecastTypes.

AutoML

If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function, set PerformAutoML to true. The objective function is defined as the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses. For more information, see EvaluationResult.

When AutoML is enabled, the following properties are disallowed:

• AlgorithmArn

• HPOConfig

• PerformHPO

• TrainingParameters

To get a list of all of your predictors, use the ListPredictors operation.

Before you can use the predictor to create a forecast, the Status of the predictor must be ACTIVE, signifying that training has completed. To get the status, use the DescribePredictor operation.

Implementations

impl CreatePredictor

pub async fn customize(
    self
) -> Result<CustomizableOperation<CreatePredictor, AwsResponseRetryClassifier>, SdkError<CreatePredictorError>>

Consume this builder, creating a customizable operation that can be modified before being sent. The operation’s inner http::Request can be modified as well.

pub async fn send(
    self
) -> Result<CreatePredictorOutput, SdkError<CreatePredictorError>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn predictor_name(self, input: impl Into<String>) -> Self

A name for the predictor.

pub fn set_predictor_name(self, input: Option<String>) -> Self

A name for the predictor.

pub fn algorithm_arn(self, input: impl Into<String>) -> Self

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/CNN-QR

• arn:aws:forecast:::algorithm/Deep_AR_Plus

• arn:aws:forecast:::algorithm/ETS

• arn:aws:forecast:::algorithm/NPTS

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

pub fn set_algorithm_arn(self, input: Option<String>) -> Self

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/CNN-QR

• arn:aws:forecast:::algorithm/Deep_AR_Plus

• arn:aws:forecast:::algorithm/ETS

• arn:aws:forecast:::algorithm/NPTS

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

pub fn forecast_horizon(self, input: i32) -> Self

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.

pub fn set_forecast_horizon(self, input: Option<i32>) -> Self

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.

pub fn forecast_types(self, input: impl Into<String>) -> Self

Appends an item to ForecastTypes.

To override the contents of this collection use set_forecast_types.

Specifies the forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean.

The default value is ["0.10", "0.50", "0.9"].

pub fn set_forecast_types(self, input: Option<Vec<String>>) -> Self

Specifies the forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean.

The default value is ["0.10", "0.50", "0.9"].

pub fn perform_auto_ml(self, input: bool) -> Self

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.

pub fn set_perform_auto_ml(self, input: Option<bool>) -> Self

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.

pub fn auto_ml_override_strategy(self, input: AutoMlOverrideStrategy) -> Self

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

Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use LatencyOptimized.

This parameter is only valid for predictors trained using AutoML.

pub fn set_auto_ml_override_strategy(
    self,
    input: Option<AutoMlOverrideStrategy>
) -> Self

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

Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use LatencyOptimized.

This parameter is only valid for predictors trained using AutoML.

pub fn perform_hpo(self, input: bool) -> Self

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 algorithms support HPO:

• DeepAR+

• CNN-QR

pub fn set_perform_hpo(self, input: Option<bool>) -> Self

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 algorithms support HPO:

• DeepAR+

• CNN-QR

pub fn training_parameters(
    self,
    k: impl Into<String>,
    v: impl Into<String>
) -> Self

Adds a key-value pair to TrainingParameters.

To override the contents of this collection use set_training_parameters.

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.

pub fn set_training_parameters(
    self,
    input: Option<HashMap<String, String>>
) -> Self

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.

pub fn evaluation_parameters(self, input: EvaluationParameters) -> Self

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 set_evaluation_parameters(
    self,
    input: Option<EvaluationParameters>
) -> Self

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, input: HyperParameterTuningJobConfig) -> Self

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.

pub fn set_hpo_config(self, input: Option<HyperParameterTuningJobConfig>) -> Self

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.

pub fn input_data_config(self, input: InputDataConfig) -> Self

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

pub fn set_input_data_config(self, input: Option<InputDataConfig>) -> Self

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

pub fn featurization_config(self, input: FeaturizationConfig) -> Self

The featurization configuration.

pub fn set_featurization_config(self, input: Option<FeaturizationConfig>) -> Self

The featurization configuration.

pub fn encryption_config(self, input: EncryptionConfig) -> Self

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.

pub fn set_encryption_config(self, input: Option<EncryptionConfig>) -> Self

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.

pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

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.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

pub fn optimization_metric(self, input: OptimizationMetric) -> Self

The accuracy metric used to optimize the predictor.

pub fn set_optimization_metric(self, input: Option<OptimizationMetric>) -> Self

The accuracy metric used to optimize the predictor.

Trait Implementations

impl Clone for CreatePredictor

fn clone(&self) -> CreatePredictor

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreatePredictor

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

Formats the value using the given formatter.