Class: Aws::ForecastService::Types::CreatePredictorRequest
- Inherits:
-
Struct
- Object
- Struct
- Aws::ForecastService::Types::CreatePredictorRequest
- Defined in:
- gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb
Overview
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#algorithm_arn ⇒ String
The Amazon Resource Name (ARN) of the algorithm to use for model training.
-
#auto_ml_override_strategy ⇒ String
The LatencyOptimized
AutoML override strategy is only available in private beta. -
#encryption_config ⇒ Types::EncryptionConfig
An Key Management Service (KMS) key and the 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.
-
#featurization_config ⇒ Types::FeaturizationConfig
The featurization configuration.
-
#forecast_horizon ⇒ Integer
Specifies the number of time-steps that the model is trained to predict.
-
#forecast_types ⇒ Array<String>
Specifies the forecast types used to train a predictor.
-
#hpo_config ⇒ Types::HyperParameterTuningJobConfig
Provides hyperparameter override values for the algorithm.
-
#input_data_config ⇒ Types::InputDataConfig
Describes the dataset group that contains the data to use to train the predictor.
-
#optimization_metric ⇒ String
The accuracy metric used to optimize the predictor.
-
#perform_auto_ml ⇒ Boolean
Whether to perform AutoML.
-
#perform_hpo ⇒ Boolean
Whether to perform hyperparameter optimization (HPO).
-
#predictor_name ⇒ String
A name for the predictor.
-
#tags ⇒ Array<Types::Tag>
The optional metadata that you apply to the predictor to help you categorize and organize them.
-
#training_parameters ⇒ Hash<String,String>
The hyperparameters to override for model training.
Instance Attribute Details
#algorithm_arn ⇒ 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/CNN-QR
arn:aws:forecast:::algorithm/Deep_AR_Plus
arn:aws:forecast:::algorithm/ETS
arn:aws:forecast:::algorithm/NPTS
arn:aws:forecast:::algorithm/Prophet
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#auto_ml_override_strategy ⇒ String
LatencyOptimized
AutoML override strategy is only available in
private beta. Contact Amazon Web Services 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.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#encryption_config ⇒ Types::EncryptionConfig
An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#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.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#featurization_config ⇒ Types::FeaturizationConfig
The featurization configuration.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#forecast_horizon ⇒ Integer
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.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#forecast_types ⇒ Array<String>
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"]
.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#hpo_config ⇒ Types::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.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#input_data_config ⇒ Types::InputDataConfig
Describes the dataset group that contains the data to use to train the predictor.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#optimization_metric ⇒ String
The accuracy metric used to optimize the predictor.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#perform_auto_ml ⇒ Boolean
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.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#perform_hpo ⇒ Boolean
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
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#predictor_name ⇒ String
A name for the predictor.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#tags ⇒ Array<Types::Tag>
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 Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value hasaws
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 ofaws
do not count against your tags per resource limit.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |
#training_parameters ⇒ Hash<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.
1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 |
# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1630 class CreatePredictorRequest < Struct.new( :predictor_name, :algorithm_arn, :forecast_horizon, :forecast_types, :perform_auto_ml, :auto_ml_override_strategy, :perform_hpo, :training_parameters, :evaluation_parameters, :hpo_config, :input_data_config, :featurization_config, :encryption_config, :tags, :optimization_metric) SENSITIVE = [] include Aws::Structure end |