com.amazonaws.services.forecast.model

Class CreatePredictorRequest

java.lang.Object

com.amazonaws.AmazonWebServiceRequest

com.amazonaws.services.forecast.model.CreatePredictorRequest

All Implemented Interfaces:

HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class CreatePredictorRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable

See Also:

AWS API Documentation, Serialized Form

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

NOOP

Constructor Summary

Constructors

Constructor and Description
CreatePredictorRequest()

Method Summary

Modifier and Type	Method and Description
CreatePredictorRequest	addTrainingParametersEntry(String key, String value) Add a single TrainingParameters entry
CreatePredictorRequest	clearTrainingParametersEntries() Removes all the entries added into TrainingParameters.
CreatePredictorRequest	clone() Creates a shallow clone of this object for all fields except the handler context.
boolean	equals(Object obj)
String	getAlgorithmArn() The Amazon Resource Name (ARN) of the algorithm to use for model training.
String	getAutoMLOverrideStrategy()
EncryptionConfig	getEncryptionConfig() An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
EvaluationParameters	getEvaluationParameters() Used to override the default evaluation parameters of the specified algorithm.
FeaturizationConfig	getFeaturizationConfig() The featurization configuration.
Integer	getForecastHorizon() Specifies the number of time-steps that the model is trained to predict.
List<String>	getForecastTypes() Specifies the forecast types used to train a predictor.
HyperParameterTuningJobConfig	getHPOConfig() Provides hyperparameter override values for the algorithm.
InputDataConfig	getInputDataConfig() Describes the dataset group that contains the data to use to train the predictor.
String	getOptimizationMetric() The accuracy metric used to optimize the predictor.
Boolean	getPerformAutoML() Whether to perform AutoML.
Boolean	getPerformHPO() Whether to perform hyperparameter optimization (HPO).
String	getPredictorName() A name for the predictor.
List<Tag>	getTags() The optional metadata that you apply to the predictor to help you categorize and organize them.
Map<String,String>	getTrainingParameters() The hyperparameters to override for model training.
int	hashCode()
Boolean	isPerformAutoML() Whether to perform AutoML.
Boolean	isPerformHPO() Whether to perform hyperparameter optimization (HPO).
void	setAlgorithmArn(String algorithmArn) The Amazon Resource Name (ARN) of the algorithm to use for model training.
void	setAutoMLOverrideStrategy(String autoMLOverrideStrategy)
void	setEncryptionConfig(EncryptionConfig encryptionConfig) An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
void	setEvaluationParameters(EvaluationParameters evaluationParameters) Used to override the default evaluation parameters of the specified algorithm.
void	setFeaturizationConfig(FeaturizationConfig featurizationConfig) The featurization configuration.
void	setForecastHorizon(Integer forecastHorizon) Specifies the number of time-steps that the model is trained to predict.
void	setForecastTypes(Collection<String> forecastTypes) Specifies the forecast types used to train a predictor.
void	setHPOConfig(HyperParameterTuningJobConfig hPOConfig) Provides hyperparameter override values for the algorithm.
void	setInputDataConfig(InputDataConfig inputDataConfig) Describes the dataset group that contains the data to use to train the predictor.
void	setOptimizationMetric(String optimizationMetric) The accuracy metric used to optimize the predictor.
void	setPerformAutoML(Boolean performAutoML) Whether to perform AutoML.
void	setPerformHPO(Boolean performHPO) Whether to perform hyperparameter optimization (HPO).
void	setPredictorName(String predictorName) A name for the predictor.
void	setTags(Collection<Tag> tags) The optional metadata that you apply to the predictor to help you categorize and organize them.
void	setTrainingParameters(Map<String,String> trainingParameters) The hyperparameters to override for model training.
String	toString() Returns a string representation of this object.
CreatePredictorRequest	withAlgorithmArn(String algorithmArn) The Amazon Resource Name (ARN) of the algorithm to use for model training.
CreatePredictorRequest	withAutoMLOverrideStrategy(AutoMLOverrideStrategy autoMLOverrideStrategy)
CreatePredictorRequest	withAutoMLOverrideStrategy(String autoMLOverrideStrategy)
CreatePredictorRequest	withEncryptionConfig(EncryptionConfig encryptionConfig) An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
CreatePredictorRequest	withEvaluationParameters(EvaluationParameters evaluationParameters) Used to override the default evaluation parameters of the specified algorithm.
CreatePredictorRequest	withFeaturizationConfig(FeaturizationConfig featurizationConfig) The featurization configuration.
CreatePredictorRequest	withForecastHorizon(Integer forecastHorizon) Specifies the number of time-steps that the model is trained to predict.
CreatePredictorRequest	withForecastTypes(Collection<String> forecastTypes) Specifies the forecast types used to train a predictor.
CreatePredictorRequest	withForecastTypes(String... forecastTypes) Specifies the forecast types used to train a predictor.
CreatePredictorRequest	withHPOConfig(HyperParameterTuningJobConfig hPOConfig) Provides hyperparameter override values for the algorithm.
CreatePredictorRequest	withInputDataConfig(InputDataConfig inputDataConfig) Describes the dataset group that contains the data to use to train the predictor.
CreatePredictorRequest	withOptimizationMetric(OptimizationMetric optimizationMetric) The accuracy metric used to optimize the predictor.
CreatePredictorRequest	withOptimizationMetric(String optimizationMetric) The accuracy metric used to optimize the predictor.
CreatePredictorRequest	withPerformAutoML(Boolean performAutoML) Whether to perform AutoML.
CreatePredictorRequest	withPerformHPO(Boolean performHPO) Whether to perform hyperparameter optimization (HPO).
CreatePredictorRequest	withPredictorName(String predictorName) A name for the predictor.
CreatePredictorRequest	withTags(Collection<Tag> tags) The optional metadata that you apply to the predictor to help you categorize and organize them.
CreatePredictorRequest	withTags(Tag... tags) The optional metadata that you apply to the predictor to help you categorize and organize them.
CreatePredictorRequest	withTrainingParameters(Map<String,String> trainingParameters) The hyperparameters to override for model training.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CreatePredictorRequest

public CreatePredictorRequest()

Method Detail

setPredictorName

public void setPredictorName(String predictorName)

A name for the predictor.

Parameters:

predictorName - A name for the predictor.

getPredictorName

public String getPredictorName()

A name for the predictor.

Returns:

A name for the predictor.

withPredictorName

public CreatePredictorRequest withPredictorName(String predictorName)

A name for the predictor.

Parameters:

predictorName - A name for the predictor.

Returns:

Returns a reference to this object so that method calls can be chained together.

setAlgorithmArn

public void setAlgorithmArn(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

Parameters:

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

getAlgorithmArn

public String getAlgorithmArn()

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

withAlgorithmArn

public CreatePredictorRequest withAlgorithmArn(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

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

setForecastHorizon

public void setForecastHorizon(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.

Parameters:

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.

getForecastHorizon

public Integer getForecastHorizon()

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.

withForecastHorizon

public CreatePredictorRequest withForecastHorizon(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.

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

getForecastTypes

public List<String> getForecastTypes()

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"].

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"].

setForecastTypes

public void setForecastTypes(Collection<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"].

Parameters:

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"].

withForecastTypes

public CreatePredictorRequest withForecastTypes(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"].

NOTE: This method appends the values to the existing list (if any). Use setForecastTypes(java.util.Collection) or withForecastTypes(java.util.Collection) if you want to override the existing values.

Parameters:

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"].

Returns:

Returns a reference to this object so that method calls can be chained together.

withForecastTypes

public CreatePredictorRequest withForecastTypes(Collection<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"].

Parameters:

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"].

Returns:

Returns a reference to this object so that method calls can be chained together.

setPerformAutoML

public void setPerformAutoML(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.

Parameters:

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.

getPerformAutoML

public Boolean getPerformAutoML()

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.

withPerformAutoML

public CreatePredictorRequest withPerformAutoML(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.

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

isPerformAutoML

public Boolean isPerformAutoML()

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.

setAutoMLOverrideStrategy

public void setAutoMLOverrideStrategy(String autoMLOverrideStrategy)

The 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.

Parameters:

autoMLOverrideStrategy -

The 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.

See Also:

AutoMLOverrideStrategy

getAutoMLOverrideStrategy

public String getAutoMLOverrideStrategy()

The 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.

Returns:

The 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.

See Also:

AutoMLOverrideStrategy

withAutoMLOverrideStrategy

public CreatePredictorRequest withAutoMLOverrideStrategy(String autoMLOverrideStrategy)

The 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.

Parameters:

autoMLOverrideStrategy -

The 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

AutoMLOverrideStrategy

withAutoMLOverrideStrategy

public CreatePredictorRequest withAutoMLOverrideStrategy(AutoMLOverrideStrategy autoMLOverrideStrategy)

The 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.

Parameters:

autoMLOverrideStrategy -

The 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

AutoMLOverrideStrategy

setPerformHPO

public void setPerformHPO(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

Parameters:

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

getPerformHPO

public Boolean getPerformHPO()

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

withPerformHPO

public CreatePredictorRequest withPerformHPO(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

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

isPerformHPO

public Boolean isPerformHPO()

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

getTrainingParameters

public Map<String,String> getTrainingParameters()

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.

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.

setTrainingParameters

public void setTrainingParameters(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.

Parameters:

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.

withTrainingParameters

public CreatePredictorRequest withTrainingParameters(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.

Parameters:

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.

Returns:

Returns a reference to this object so that method calls can be chained together.

addTrainingParametersEntry

public CreatePredictorRequest addTrainingParametersEntry(String key,
                                                         String value)

Add a single TrainingParameters entry

See Also:

withTrainingParameters(java.util.Map<java.lang.String, java.lang.String>)

clearTrainingParametersEntries

public CreatePredictorRequest clearTrainingParametersEntries()

Removes all the entries added into TrainingParameters.

Returns:

Returns a reference to this object so that method calls can be chained together.

setEvaluationParameters

public void setEvaluationParameters(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.

Parameters:

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.

getEvaluationParameters

public EvaluationParameters getEvaluationParameters()

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.

withEvaluationParameters

public CreatePredictorRequest withEvaluationParameters(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.

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

setHPOConfig

public void setHPOConfig(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.

Parameters:

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.

getHPOConfig

public HyperParameterTuningJobConfig getHPOConfig()

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.

withHPOConfig

public CreatePredictorRequest withHPOConfig(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.

Parameters:

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:

Returns a reference to this object so that method calls can be chained together.

setInputDataConfig

public void setInputDataConfig(InputDataConfig inputDataConfig)

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

Parameters:

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

getInputDataConfig

public InputDataConfig getInputDataConfig()

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.

withInputDataConfig

public CreatePredictorRequest withInputDataConfig(InputDataConfig inputDataConfig)

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

Parameters:

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

Returns:

Returns a reference to this object so that method calls can be chained together.

setFeaturizationConfig

public void setFeaturizationConfig(FeaturizationConfig featurizationConfig)

The featurization configuration.

Parameters:

featurizationConfig - The featurization configuration.

getFeaturizationConfig

public FeaturizationConfig getFeaturizationConfig()

The featurization configuration.

Returns:

The featurization configuration.

withFeaturizationConfig

public CreatePredictorRequest withFeaturizationConfig(FeaturizationConfig featurizationConfig)

The featurization configuration.

Parameters:

featurizationConfig - The featurization configuration.

Returns:

Returns a reference to this object so that method calls can be chained together.

setEncryptionConfig

public void setEncryptionConfig(EncryptionConfig encryptionConfig)

An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

Parameters:

encryptionConfig - An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

getEncryptionConfig

public EncryptionConfig getEncryptionConfig()

An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

Returns:

An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

withEncryptionConfig

public CreatePredictorRequest withEncryptionConfig(EncryptionConfig encryptionConfig)

An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

Parameters:

encryptionConfig - An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

Returns:

Returns a reference to this object so that method calls can be chained together.

getTags

public List<Tag> getTags()

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 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.

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 Amazon Web Services 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.

setTags

public void setTags(Collection<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 Amazon Web Services 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.

Parameters:

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 Amazon Web Services 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.

withTags

public CreatePredictorRequest withTags(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 Amazon Web Services 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.

NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.

Parameters:

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 Amazon Web Services 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

withTags

public CreatePredictorRequest withTags(Collection<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 Amazon Web Services 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.

Parameters:

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 Amazon Web Services 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.

Returns:

Returns a reference to this object so that method calls can be chained together.

setOptimizationMetric

public void setOptimizationMetric(String optimizationMetric)

The accuracy metric used to optimize the predictor.

Parameters:

optimizationMetric - The accuracy metric used to optimize the predictor.

See Also:

OptimizationMetric

getOptimizationMetric

public String getOptimizationMetric()

The accuracy metric used to optimize the predictor.

Returns:

The accuracy metric used to optimize the predictor.

See Also:

OptimizationMetric

withOptimizationMetric

public CreatePredictorRequest withOptimizationMetric(String optimizationMetric)

The accuracy metric used to optimize the predictor.

Parameters:

optimizationMetric - The accuracy metric used to optimize the predictor.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

OptimizationMetric

withOptimizationMetric

public CreatePredictorRequest withOptimizationMetric(OptimizationMetric optimizationMetric)

The accuracy metric used to optimize the predictor.

Parameters:

optimizationMetric - The accuracy metric used to optimize the predictor.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

OptimizationMetric

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

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public CreatePredictorRequest clone()

Description copied from class: AmazonWebServiceRequest

Creates a shallow clone of this object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.

Overrides:

clone in class AmazonWebServiceRequest

See Also:

Object.clone()