[−][src]Struct rusoto_forecast::CreatePredictorRequest
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.
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
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fn clone(&self) -> CreatePredictorRequest
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fn clone_from(&mut self, source: &Self)
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impl Debug for CreatePredictorRequest
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impl Default for CreatePredictorRequest
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fn default() -> CreatePredictorRequest
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impl PartialEq<CreatePredictorRequest> for CreatePredictorRequest
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fn eq(&self, other: &CreatePredictorRequest) -> bool
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fn ne(&self, other: &CreatePredictorRequest) -> bool
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impl Serialize for CreatePredictorRequest
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fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
__S: Serializer,
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__S: Serializer,
impl StructuralPartialEq for CreatePredictorRequest
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Auto Trait Implementations
impl RefUnwindSafe for CreatePredictorRequest
impl Send for CreatePredictorRequest
impl Sync for CreatePredictorRequest
impl Unpin for CreatePredictorRequest
impl UnwindSafe for CreatePredictorRequest
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> Same<T> for T
type Output = T
Should always be Self
impl<T> Sealed<T> for T where
T: ?Sized,
T: ?Sized,
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,