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Class: Aws::ForecastService::Types::DescribePredictorResponse

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_arn ⇒ String

The Amazon Resource Name (ARN) of the algorithm used for model training.

Returns:

  • (String) —

    The Amazon Resource Name (ARN) of the algorithm used for model training.

#auto_ml_algorithm_arns ⇒ Array<String>

When PerformAutoML is specified, the ARN of the chosen algorithm.

Returns:

  • (Array<String>) —

    When PerformAutoML is specified, the ARN of the chosen algorithm.

#creation_time ⇒ Time

When the model training task was created.

Returns:

  • (Time) —

    When the model training task was created.

#dataset_import_job_arns ⇒ Array<String>

An array of the ARNs of the dataset import jobs used to import training data for the predictor.

Returns:

  • (Array<String>) —

    An array of the ARNs of the dataset import jobs used to import training data for the predictor.

#encryption_config ⇒ Types::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.

Returns:

  • (Types::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 ⇒ Types::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.

Returns:

#featurization_config ⇒ Types::FeaturizationConfig

The featurization configuration.

Returns:

#forecast_horizon ⇒ Integer

The number of time-steps of the forecast. The forecast horizon is also called the prediction length.

Returns:

  • (Integer) —

    The number of time-steps of the forecast.

#forecast_types ⇒ Array<String>

The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"]

Returns:

  • (Array<String>) —

    The forecast types used during predictor training.

#hpo_config ⇒ Types::HyperParameterTuningJobConfig

The hyperparameter override values for the algorithm.

Returns:

#input_data_config ⇒ Types::InputDataConfig

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

Returns:

  • (Types::InputDataConfig) —

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

#last_modification_time ⇒ Time

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).

Returns:

  • (Time) —

    Initially, the same as CreationTime (when the status is CREATE_PENDING).

#message ⇒ String

If an error occurred, an informational message about the error.

Returns:

  • (String) —

    If an error occurred, an informational message about the error.

#perform_auto_ml ⇒ Boolean

Whether the predictor is set to perform AutoML.

Returns:

  • (Boolean) —

    Whether the predictor is set to perform AutoML.

#perform_hpo ⇒ Boolean

Whether the predictor is set to perform hyperparameter optimization (HPO).

Returns:

  • (Boolean) —

    Whether the predictor is set to perform hyperparameter optimization (HPO).

#predictor_arn ⇒ String

The ARN of the predictor.

Returns:

  • (String) —

    The ARN of the predictor.

#predictor_execution_details ⇒ Types::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.

Returns:

#predictor_name ⇒ String

The name of the predictor.

Returns:

  • (String) —

    The name of the predictor.

#status ⇒ String

The status of the predictor. States include:

  • ACTIVE

  • CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED

  • DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED

  • UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED

The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.

Returns:

  • (String) —

    The status of the predictor.

#training_parameters ⇒ Hash<String,String>

The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.

Returns:

  • (Hash<String,String>) —

    The default training parameters or overrides selected during model training.