[−][src]Struct rusoto_forecast::DescribePredictorResponse
Fields
algorithm_arn: Option<String>
The Amazon Resource Name (ARN) of the algorithm used for model training.
auto_ml_algorithm_arns: Option<Vec<String>>
When PerformAutoML
is specified, the ARN of the chosen algorithm.
creation_time: Option<f64>
When the model training task was created.
dataset_import_job_arns: Option<Vec<String>>
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
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: Option<FeaturizationConfig>
The featurization configuration.
forecast_horizon: Option<i64>
The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
hpo_config: Option<HyperParameterTuningJobConfig>
The hyperparameter override values for the algorithm.
input_data_config: Option<InputDataConfig>
Describes the dataset group that contains the data to use to train the predictor.
last_modification_time: Option<f64>
Initially, the same as CreationTime
(when the status is CREATE_PENDING
). This value is updated when training starts (when the status changes to CREATE_IN_PROGRESS
), and when training has completed (when the status changes to ACTIVE
) or fails (when the status changes to CREATE_FAILED
).
message: Option<String>
If an error occurred, an informational message about the error.
perform_auto_ml: Option<bool>
Whether the predictor is set to perform AutoML.
perform_hpo: Option<bool>
Whether the predictor is set to perform hyperparameter optimization (HPO).
predictor_arn: Option<String>
The ARN of the predictor.
predictor_execution_details: Option<PredictorExecutionDetails>
Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.
predictor_name: Option<String>
The name of the predictor.
status: Option<String>
The status of the predictor. States include:
-
ACTIVE
-
CREATEPENDING
,CREATEINPROGRESS
,CREATEFAILED
-
DELETEPENDING
,DELETEINPROGRESS
,DELETEFAILED
-
UPDATEPENDING
,UPDATEINPROGRESS
,UPDATEFAILED
The Status
of the predictor must be ACTIVE
before you can use the predictor to create a forecast.
training_parameters: Option<HashMap<String, String>>
The default training parameters or overrides selected during model training. If using the AutoML algorithm or if HPO is turned on while using the DeepAR+ algorithms, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
Trait Implementations
impl Clone for DescribePredictorResponse
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pub fn clone(&self) -> DescribePredictorResponse
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pub fn clone_from(&mut self, source: &Self)
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impl Debug for DescribePredictorResponse
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impl Default for DescribePredictorResponse
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pub fn default() -> DescribePredictorResponse
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impl<'de> Deserialize<'de> for DescribePredictorResponse
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pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
impl PartialEq<DescribePredictorResponse> for DescribePredictorResponse
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pub fn eq(&self, other: &DescribePredictorResponse) -> bool
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pub fn ne(&self, other: &DescribePredictorResponse) -> bool
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impl StructuralPartialEq for DescribePredictorResponse
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Auto Trait Implementations
impl RefUnwindSafe for DescribePredictorResponse
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impl Send for DescribePredictorResponse
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impl Sync for DescribePredictorResponse
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impl Unpin for DescribePredictorResponse
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impl UnwindSafe for DescribePredictorResponse
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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,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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T: for<'de> Deserialize<'de>,
impl<T> From<T> for T
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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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> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub 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.
pub 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>,