software.amazon.awssdk.services.forecast.model

Class CreatePredictorRequest

java.lang.Object

software.amazon.awssdk.core.SdkRequest

software.amazon.awssdk.awscore.AwsRequest

software.amazon.awssdk.services.forecast.model.ForecastRequest

software.amazon.awssdk.services.forecast.model.CreatePredictorRequest

All Implemented Interfaces:

SdkPojo, ToCopyableBuilder<CreatePredictorRequest.Builder,CreatePredictorRequest>

@Generated(value="software.amazon.awssdk:codegen")
public final class CreatePredictorRequest
extends ForecastRequest
implements ToCopyableBuilder<CreatePredictorRequest.Builder,CreatePredictorRequest>

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	CreatePredictorRequest.Builder

Method Summary

Modifier and Type	Method and Description
String	algorithmArn() The Amazon Resource Name (ARN) of the algorithm to use for model training.
static CreatePredictorRequest.Builder	builder()
EncryptionConfig	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.
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
EvaluationParameters	evaluationParameters() Used to override the default evaluation parameters of the specified algorithm.
FeaturizationConfig	featurizationConfig() The featurization configuration.
Integer	forecastHorizon() Specifies the number of time-steps that the model is trained to predict.
List<String>	forecastTypes() Specifies the forecast types used to train a predictor.
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
boolean	hasForecastTypes() Returns true if the ForecastTypes property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty).
int	hashCode()
boolean	hasTags() Returns true if the Tags property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty).
boolean	hasTrainingParameters() Returns true if the TrainingParameters property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty).
HyperParameterTuningJobConfig	hpoConfig() Provides hyperparameter override values for the algorithm.
InputDataConfig	inputDataConfig() Describes the dataset group that contains the data to use to train the predictor.
Boolean	performAutoML() Whether to perform AutoML.
Boolean	performHPO() Whether to perform hyperparameter optimization (HPO).
String	predictorName() A name for the predictor.
List<SdkField<?>>	sdkFields()
static Class<? extends CreatePredictorRequest.Builder>	serializableBuilderClass()
List<Tag>	tags() The optional metadata that you apply to the predictor to help you categorize and organize them.
CreatePredictorRequest.Builder	toBuilder()
String	toString() Returns a string representation of this object.
Map<String,String>	trainingParameters() The hyperparameters to override for model training.

Methods inherited from class software.amazon.awssdk.awscore.AwsRequest

overrideConfiguration

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

predictorName

public String predictorName()

A name for the predictor.

Returns:

A name for the predictor.

algorithmArn

public String algorithmArn()

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

Returns:

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

forecastHorizon

public Integer forecastHorizon()

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.

Returns:

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.

hasForecastTypes

public boolean hasForecastTypes()

Returns true if the ForecastTypes property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty). For responses returned by the SDK, the sender is the AWS service.

forecastTypes

public List<String> forecastTypes()

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"].

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

You can use hasForecastTypes() to see if a value was sent in this field.

Returns:

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"].

performAutoML

public Boolean performAutoML()

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.

Returns:

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.

performHPO

public Boolean performHPO()

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

Returns:

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

hasTrainingParameters

public boolean hasTrainingParameters()

Returns true if the TrainingParameters property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty). For responses returned by the SDK, the sender is the AWS service.

trainingParameters

public Map<String,String> trainingParameters()

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.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

You can use hasTrainingParameters() to see if a value was sent in this field.

Returns:

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.

evaluationParameters

public EvaluationParameters 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:

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.

hpoConfig

public HyperParameterTuningJobConfig hpoConfig()

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.

Returns:

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.

inputDataConfig

public InputDataConfig inputDataConfig()

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

Returns:

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

featurizationConfig

public FeaturizationConfig featurizationConfig()

The featurization configuration.

Returns:

The featurization configuration.

encryptionConfig

public EncryptionConfig 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:

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.

hasTags

public boolean hasTags()

Returns true if the Tags property was specified by the sender (it may be empty), or false if the sender did not specify the value (it will be empty). For responses returned by the SDK, the sender is the AWS service.

tags

public List<Tag> tags()

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 AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws 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 aws do not count against your tags per resource limit.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

You can use hasTags() to see if a value was sent in this field.

Returns:

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 AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws 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 aws do not count against your tags per resource limit.

toBuilder

public CreatePredictorRequest.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<CreatePredictorRequest.Builder,CreatePredictorRequest>

Specified by:

toBuilder in class ForecastRequest

builder

public static CreatePredictorRequest.Builder builder()

serializableBuilderClass

public static Class<? extends CreatePredictorRequest.Builder> serializableBuilderClass()

hashCode

public int hashCode()

Overrides:

hashCode in class AwsRequest

equals

public boolean equals(Object obj)

Overrides:

equals in class AwsRequest

equalsBySdkFields

public boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

getValueForField

public <T> Optional<T> getValueForField(String fieldName,
                                        Class<T> clazz)

Overrides:

getValueForField in class SdkRequest

sdkFields

public List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo