// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_predictor::_create_predictor_output::CreatePredictorOutputBuilder;

pub use crate::operation::create_predictor::_create_predictor_input::CreatePredictorInputBuilder;

impl crate::operation::create_predictor::builders::CreatePredictorInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::create_predictor::CreatePredictorOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_predictor::CreatePredictorError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_predictor();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreatePredictor`.
///
/// <note>
/// <p>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 <code>CreateAutoPredictor</code>.</p>
/// </note>
/// <p>Creates an Amazon Forecast predictor.</p>
/// <p>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.</p>
/// <p>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 <code>CreateForecast</code> operation.</p>
/// <p>To see the evaluation metrics, use the <code>GetAccuracyMetrics</code> operation.</p>
/// <p>You can specify a featurization configuration to fill and aggregate the data fields in the <code>TARGET_TIME_SERIES</code> dataset to improve model training. For more information, see <code>FeaturizationConfig</code>.</p>
/// <p>For RELATED_TIME_SERIES datasets, <code>CreatePredictor</code> verifies that the <code>DataFrequency</code> specified when the dataset was created matches the <code>ForecastFrequency</code>. TARGET_TIME_SERIES datasets don't have this restriction. Amazon Forecast also verifies the delimiter and timestamp format. For more information, see <code>howitworks-datasets-groups</code>.</p>
/// <p>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 <code>ForecastTypes</code>.</p>
/// <p><b>AutoML</b></p>
/// <p>If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the <code>objective function</code>, set <code>PerformAutoML</code> to <code>true</code>. The <code>objective function</code> 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 <code>EvaluationResult</code>.</p>
/// <p>When AutoML is enabled, the following properties are disallowed:</p>
/// <ul>
/// <li>
/// <p><code>AlgorithmArn</code></p></li>
/// <li>
/// <p><code>HPOConfig</code></p></li>
/// <li>
/// <p><code>PerformHPO</code></p></li>
/// <li>
/// <p><code>TrainingParameters</code></p></li>
/// </ul>
/// <p>To get a list of all of your predictors, use the <code>ListPredictors</code> operation.</p><note>
/// <p>Before you can use the predictor to create a forecast, the <code>Status</code> of the predictor must be <code>ACTIVE</code>, signifying that training has completed. To get the status, use the <code>DescribePredictor</code> operation.</p>
/// </note>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreatePredictorFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_predictor::builders::CreatePredictorInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_predictor::CreatePredictorOutput,
        crate::operation::create_predictor::CreatePredictorError,
    > for CreatePredictorFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_predictor::CreatePredictorOutput,
            crate::operation::create_predictor::CreatePredictorError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreatePredictorFluentBuilder {
    /// Creates a new `CreatePredictorFluentBuilder`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the CreatePredictor as a reference.
    pub fn as_input(&self) -> &crate::operation::create_predictor::builders::CreatePredictorInputBuilder {
        &self.inner
    }
    /// 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](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::create_predictor::CreatePredictorOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_predictor::CreatePredictorError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::create_predictor::CreatePredictor::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_predictor::CreatePredictor::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::create_predictor::CreatePredictorOutput,
        crate::operation::create_predictor::CreatePredictorError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
        self.set_config_override(::std::option::Option::Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>A name for the predictor.</p>
    pub fn predictor_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.predictor_name(input.into());
        self
    }
    /// <p>A name for the predictor.</p>
    pub fn set_predictor_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_predictor_name(input);
        self
    }
    /// <p>A name for the predictor.</p>
    pub fn get_predictor_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_predictor_name()
    }
    /// <p>The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if <code>PerformAutoML</code> is not set to <code>true</code>.</p>
    /// <p class="title"><b>Supported algorithms:</b></p>
    /// <ul>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/ARIMA</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/CNN-QR</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/Deep_AR_Plus</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/ETS</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/NPTS</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/Prophet</code></p></li>
    /// </ul>
    pub fn algorithm_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.algorithm_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if <code>PerformAutoML</code> is not set to <code>true</code>.</p>
    /// <p class="title"><b>Supported algorithms:</b></p>
    /// <ul>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/ARIMA</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/CNN-QR</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/Deep_AR_Plus</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/ETS</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/NPTS</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/Prophet</code></p></li>
    /// </ul>
    pub fn set_algorithm_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_algorithm_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if <code>PerformAutoML</code> is not set to <code>true</code>.</p>
    /// <p class="title"><b>Supported algorithms:</b></p>
    /// <ul>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/ARIMA</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/CNN-QR</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/Deep_AR_Plus</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/ETS</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/NPTS</code></p></li>
    /// <li>
    /// <p><code>arn:aws:forecast:::algorithm/Prophet</code></p></li>
    /// </ul>
    pub fn get_algorithm_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_algorithm_arn()
    }
    /// <p>Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.</p>
    /// <p>For example, if you configure a dataset for daily data collection (using the <code>DataFrequency</code> parameter of the <code>CreateDataset</code> operation) and set the forecast horizon to 10, the model returns predictions for 10 days.</p>
    /// <p>The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.</p>
    pub fn forecast_horizon(mut self, input: i32) -> Self {
        self.inner = self.inner.forecast_horizon(input);
        self
    }
    /// <p>Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.</p>
    /// <p>For example, if you configure a dataset for daily data collection (using the <code>DataFrequency</code> parameter of the <code>CreateDataset</code> operation) and set the forecast horizon to 10, the model returns predictions for 10 days.</p>
    /// <p>The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.</p>
    pub fn set_forecast_horizon(mut self, input: ::std::option::Option<i32>) -> Self {
        self.inner = self.inner.set_forecast_horizon(input);
        self
    }
    /// <p>Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.</p>
    /// <p>For example, if you configure a dataset for daily data collection (using the <code>DataFrequency</code> parameter of the <code>CreateDataset</code> operation) and set the forecast horizon to 10, the model returns predictions for 10 days.</p>
    /// <p>The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.</p>
    pub fn get_forecast_horizon(&self) -> &::std::option::Option<i32> {
        self.inner.get_forecast_horizon()
    }
    ///
    /// Appends an item to `ForecastTypes`.
    ///
    /// To override the contents of this collection use [`set_forecast_types`](Self::set_forecast_types).
    ///
    /// <p>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 <code>mean</code>.</p>
    /// <p>The default value is <code>\["0.10", "0.50", "0.9"\]</code>.</p>
    pub fn forecast_types(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.forecast_types(input.into());
        self
    }
    /// <p>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 <code>mean</code>.</p>
    /// <p>The default value is <code>\["0.10", "0.50", "0.9"\]</code>.</p>
    pub fn set_forecast_types(mut self, input: ::std::option::Option<::std::vec::Vec<::std::string::String>>) -> Self {
        self.inner = self.inner.set_forecast_types(input);
        self
    }
    /// <p>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 <code>mean</code>.</p>
    /// <p>The default value is <code>\["0.10", "0.50", "0.9"\]</code>.</p>
    pub fn get_forecast_types(&self) -> &::std::option::Option<::std::vec::Vec<::std::string::String>> {
        self.inner.get_forecast_types()
    }
    /// <p>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.</p>
    /// <p>The default value is <code>false</code>. In this case, you are required to specify an algorithm.</p>
    /// <p>Set <code>PerformAutoML</code> to <code>true</code> 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, <code>PerformHPO</code> must be false.</p>
    pub fn perform_auto_ml(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_auto_ml(input);
        self
    }
    /// <p>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.</p>
    /// <p>The default value is <code>false</code>. In this case, you are required to specify an algorithm.</p>
    /// <p>Set <code>PerformAutoML</code> to <code>true</code> 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, <code>PerformHPO</code> must be false.</p>
    pub fn set_perform_auto_ml(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_auto_ml(input);
        self
    }
    /// <p>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.</p>
    /// <p>The default value is <code>false</code>. In this case, you are required to specify an algorithm.</p>
    /// <p>Set <code>PerformAutoML</code> to <code>true</code> 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, <code>PerformHPO</code> must be false.</p>
    pub fn get_perform_auto_ml(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_auto_ml()
    }
    /// <note>
    /// <p>The <code>LatencyOptimized</code> AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.</p>
    /// </note>
    /// <p>Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use <code>LatencyOptimized</code>.</p>
    /// <p>This parameter is only valid for predictors trained using AutoML.</p>
    pub fn auto_ml_override_strategy(mut self, input: crate::types::AutoMlOverrideStrategy) -> Self {
        self.inner = self.inner.auto_ml_override_strategy(input);
        self
    }
    /// <note>
    /// <p>The <code>LatencyOptimized</code> AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.</p>
    /// </note>
    /// <p>Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use <code>LatencyOptimized</code>.</p>
    /// <p>This parameter is only valid for predictors trained using AutoML.</p>
    pub fn set_auto_ml_override_strategy(mut self, input: ::std::option::Option<crate::types::AutoMlOverrideStrategy>) -> Self {
        self.inner = self.inner.set_auto_ml_override_strategy(input);
        self
    }
    /// <note>
    /// <p>The <code>LatencyOptimized</code> AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.</p>
    /// </note>
    /// <p>Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use <code>LatencyOptimized</code>.</p>
    /// <p>This parameter is only valid for predictors trained using AutoML.</p>
    pub fn get_auto_ml_override_strategy(&self) -> &::std::option::Option<crate::types::AutoMlOverrideStrategy> {
        self.inner.get_auto_ml_override_strategy()
    }
    /// <p>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.</p>
    /// <p>The default value is <code>false</code>. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.</p>
    /// <p>To override the default values, set <code>PerformHPO</code> to <code>true</code> and, optionally, supply the <code>HyperParameterTuningJobConfig</code> 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 <code>PerformAutoML</code> must be false.</p>
    /// <p>The following algorithms support HPO:</p>
    /// <ul>
    /// <li>
    /// <p>DeepAR+</p></li>
    /// <li>
    /// <p>CNN-QR</p></li>
    /// </ul>
    pub fn perform_hpo(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_hpo(input);
        self
    }
    /// <p>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.</p>
    /// <p>The default value is <code>false</code>. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.</p>
    /// <p>To override the default values, set <code>PerformHPO</code> to <code>true</code> and, optionally, supply the <code>HyperParameterTuningJobConfig</code> 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 <code>PerformAutoML</code> must be false.</p>
    /// <p>The following algorithms support HPO:</p>
    /// <ul>
    /// <li>
    /// <p>DeepAR+</p></li>
    /// <li>
    /// <p>CNN-QR</p></li>
    /// </ul>
    pub fn set_perform_hpo(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_hpo(input);
        self
    }
    /// <p>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.</p>
    /// <p>The default value is <code>false</code>. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.</p>
    /// <p>To override the default values, set <code>PerformHPO</code> to <code>true</code> and, optionally, supply the <code>HyperParameterTuningJobConfig</code> 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 <code>PerformAutoML</code> must be false.</p>
    /// <p>The following algorithms support HPO:</p>
    /// <ul>
    /// <li>
    /// <p>DeepAR+</p></li>
    /// <li>
    /// <p>CNN-QR</p></li>
    /// </ul>
    pub fn get_perform_hpo(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_hpo()
    }
    ///
    /// Adds a key-value pair to `TrainingParameters`.
    ///
    /// To override the contents of this collection use [`set_training_parameters`](Self::set_training_parameters).
    ///
    /// <p>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 <code>aws-forecast-choosing-recipes</code>.</p>
    pub fn training_parameters(
        mut self,
        k: impl ::std::convert::Into<::std::string::String>,
        v: impl ::std::convert::Into<::std::string::String>,
    ) -> Self {
        self.inner = self.inner.training_parameters(k.into(), v.into());
        self
    }
    /// <p>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 <code>aws-forecast-choosing-recipes</code>.</p>
    pub fn set_training_parameters(
        mut self,
        input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    ) -> Self {
        self.inner = self.inner.set_training_parameters(input);
        self
    }
    /// <p>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 <code>aws-forecast-choosing-recipes</code>.</p>
    pub fn get_training_parameters(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_training_parameters()
    }
    /// <p>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.</p>
    pub fn evaluation_parameters(mut self, input: crate::types::EvaluationParameters) -> Self {
        self.inner = self.inner.evaluation_parameters(input);
        self
    }
    /// <p>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.</p>
    pub fn set_evaluation_parameters(mut self, input: ::std::option::Option<crate::types::EvaluationParameters>) -> Self {
        self.inner = self.inner.set_evaluation_parameters(input);
        self
    }
    /// <p>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.</p>
    pub fn get_evaluation_parameters(&self) -> &::std::option::Option<crate::types::EvaluationParameters> {
        self.inner.get_evaluation_parameters()
    }
    /// <p>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 <code>aws-forecast-choosing-recipes</code>.</p>
    /// <p>If you included the <code>HPOConfig</code> object, you must set <code>PerformHPO</code> to true.</p>
    pub fn hpo_config(mut self, input: crate::types::HyperParameterTuningJobConfig) -> Self {
        self.inner = self.inner.hpo_config(input);
        self
    }
    /// <p>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 <code>aws-forecast-choosing-recipes</code>.</p>
    /// <p>If you included the <code>HPOConfig</code> object, you must set <code>PerformHPO</code> to true.</p>
    pub fn set_hpo_config(mut self, input: ::std::option::Option<crate::types::HyperParameterTuningJobConfig>) -> Self {
        self.inner = self.inner.set_hpo_config(input);
        self
    }
    /// <p>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 <code>aws-forecast-choosing-recipes</code>.</p>
    /// <p>If you included the <code>HPOConfig</code> object, you must set <code>PerformHPO</code> to true.</p>
    pub fn get_hpo_config(&self) -> &::std::option::Option<crate::types::HyperParameterTuningJobConfig> {
        self.inner.get_hpo_config()
    }
    /// <p>Describes the dataset group that contains the data to use to train the predictor.</p>
    pub fn input_data_config(mut self, input: crate::types::InputDataConfig) -> Self {
        self.inner = self.inner.input_data_config(input);
        self
    }
    /// <p>Describes the dataset group that contains the data to use to train the predictor.</p>
    pub fn set_input_data_config(mut self, input: ::std::option::Option<crate::types::InputDataConfig>) -> Self {
        self.inner = self.inner.set_input_data_config(input);
        self
    }
    /// <p>Describes the dataset group that contains the data to use to train the predictor.</p>
    pub fn get_input_data_config(&self) -> &::std::option::Option<crate::types::InputDataConfig> {
        self.inner.get_input_data_config()
    }
    /// <p>The featurization configuration.</p>
    pub fn featurization_config(mut self, input: crate::types::FeaturizationConfig) -> Self {
        self.inner = self.inner.featurization_config(input);
        self
    }
    /// <p>The featurization configuration.</p>
    pub fn set_featurization_config(mut self, input: ::std::option::Option<crate::types::FeaturizationConfig>) -> Self {
        self.inner = self.inner.set_featurization_config(input);
        self
    }
    /// <p>The featurization configuration.</p>
    pub fn get_featurization_config(&self) -> &::std::option::Option<crate::types::FeaturizationConfig> {
        self.inner.get_featurization_config()
    }
    /// <p>An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.</p>
    pub fn encryption_config(mut self, input: crate::types::EncryptionConfig) -> Self {
        self.inner = self.inner.encryption_config(input);
        self
    }
    /// <p>An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.</p>
    pub fn set_encryption_config(mut self, input: ::std::option::Option<crate::types::EncryptionConfig>) -> Self {
        self.inner = self.inner.set_encryption_config(input);
        self
    }
    /// <p>An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.</p>
    pub fn get_encryption_config(&self) -> &::std::option::Option<crate::types::EncryptionConfig> {
        self.inner.get_encryption_config()
    }
    ///
    /// Appends an item to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>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.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>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: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use <code>aws:</code>, <code>AWS:</code>, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has <code>aws</code> 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 <code>aws</code> do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>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.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>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: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use <code>aws:</code>, <code>AWS:</code>, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has <code>aws</code> 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 <code>aws</code> do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p>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.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>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: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use <code>aws:</code>, <code>AWS:</code>, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has <code>aws</code> 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 <code>aws</code> do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
    /// <p>The accuracy metric used to optimize the predictor.</p>
    pub fn optimization_metric(mut self, input: crate::types::OptimizationMetric) -> Self {
        self.inner = self.inner.optimization_metric(input);
        self
    }
    /// <p>The accuracy metric used to optimize the predictor.</p>
    pub fn set_optimization_metric(mut self, input: ::std::option::Option<crate::types::OptimizationMetric>) -> Self {
        self.inner = self.inner.set_optimization_metric(input);
        self
    }
    /// <p>The accuracy metric used to optimize the predictor.</p>
    pub fn get_optimization_metric(&self) -> &::std::option::Option<crate::types::OptimizationMetric> {
        self.inner.get_optimization_metric()
    }
}