Interface DescribePredictorResponse.Builder
- All Superinterfaces:
AwsResponse.Builder
,Buildable
,CopyableBuilder<DescribePredictorResponse.Builder,
,DescribePredictorResponse> ForecastResponse.Builder
,SdkBuilder<DescribePredictorResponse.Builder,
,DescribePredictorResponse> SdkPojo
,SdkResponse.Builder
- Enclosing class:
DescribePredictorResponse
-
Method Summary
Modifier and TypeMethodDescriptionalgorithmArn
(String algorithmArn) The Amazon Resource Name (ARN) of the algorithm used for model training.autoMLAlgorithmArns
(String... autoMLAlgorithmArns) WhenPerformAutoML
is specified, the ARN of the chosen algorithm.autoMLAlgorithmArns
(Collection<String> autoMLAlgorithmArns) WhenPerformAutoML
is specified, the ARN of the chosen algorithm.autoMLOverrideStrategy
(String autoMLOverrideStrategy) autoMLOverrideStrategy
(AutoMLOverrideStrategy autoMLOverrideStrategy) creationTime
(Instant creationTime) When the model training task was created.datasetImportJobArns
(String... datasetImportJobArns) An array of the ARNs of the dataset import jobs used to import training data for the predictor.datasetImportJobArns
(Collection<String> datasetImportJobArns) An array of the ARNs of the dataset import jobs used to import training data for the predictor.encryptionConfig
(Consumer<EncryptionConfig.Builder> encryptionConfig) An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.encryptionConfig
(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.estimatedTimeRemainingInMinutes
(Long estimatedTimeRemainingInMinutes) The estimated time remaining in minutes for the predictor training job to complete.evaluationParameters
(Consumer<EvaluationParameters.Builder> evaluationParameters) Used to override the default evaluation parameters of the specified algorithm.evaluationParameters
(EvaluationParameters evaluationParameters) Used to override the default evaluation parameters of the specified algorithm.featurizationConfig
(Consumer<FeaturizationConfig.Builder> featurizationConfig) The featurization configuration.featurizationConfig
(FeaturizationConfig featurizationConfig) The featurization configuration.forecastHorizon
(Integer forecastHorizon) The number of time-steps of the forecast.forecastTypes
(String... forecastTypes) The forecast types used during predictor training.forecastTypes
(Collection<String> forecastTypes) The forecast types used during predictor training.hpoConfig
(Consumer<HyperParameterTuningJobConfig.Builder> hpoConfig) The hyperparameter override values for the algorithm.hpoConfig
(HyperParameterTuningJobConfig hpoConfig) The hyperparameter override values for the algorithm.inputDataConfig
(Consumer<InputDataConfig.Builder> inputDataConfig) Describes the dataset group that contains the data to use to train the predictor.inputDataConfig
(InputDataConfig inputDataConfig) Describes the dataset group that contains the data to use to train the predictor.isAutoPredictor
(Boolean isAutoPredictor) Whether the predictor was created with CreateAutoPredictor.lastModificationTime
(Instant lastModificationTime) The last time the resource was modified.If an error occurred, an informational message about the error.optimizationMetric
(String optimizationMetric) The accuracy metric used to optimize the predictor.optimizationMetric
(OptimizationMetric optimizationMetric) The accuracy metric used to optimize the predictor.performAutoML
(Boolean performAutoML) Whether the predictor is set to perform AutoML.performHPO
(Boolean performHPO) Whether the predictor is set to perform hyperparameter optimization (HPO).predictorArn
(String predictorArn) The ARN of the predictor.predictorExecutionDetails
(Consumer<PredictorExecutionDetails.Builder> predictorExecutionDetails) Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor.predictorExecutionDetails
(PredictorExecutionDetails predictorExecutionDetails) Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor.predictorName
(String predictorName) The name of the predictor.The status of the predictor.trainingParameters
(Map<String, String> trainingParameters) The default training parameters or overrides selected during model training.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.services.forecast.model.ForecastResponse.Builder
build, responseMetadata, responseMetadata
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
Methods inherited from interface software.amazon.awssdk.core.SdkResponse.Builder
sdkHttpResponse, sdkHttpResponse
-
Method Details
-
predictorArn
The ARN of the predictor.
- Parameters:
predictorArn
- The ARN of the predictor.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
predictorName
The name of the predictor.
- Parameters:
predictorName
- The name of the predictor.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
algorithmArn
The Amazon Resource Name (ARN) of the algorithm used for model training.
- Parameters:
algorithmArn
- The Amazon Resource Name (ARN) of the algorithm used for model training.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
autoMLAlgorithmArns
When
PerformAutoML
is specified, the ARN of the chosen algorithm.- Parameters:
autoMLAlgorithmArns
- WhenPerformAutoML
is specified, the ARN of the chosen algorithm.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
autoMLAlgorithmArns
When
PerformAutoML
is specified, the ARN of the chosen algorithm.- Parameters:
autoMLAlgorithmArns
- WhenPerformAutoML
is specified, the ARN of the chosen algorithm.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
forecastHorizon
The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
- Parameters:
forecastHorizon
- The number of time-steps of the forecast. The forecast horizon is also called the prediction length.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
forecastTypes
The forecast types used during predictor training. Default value is
["0.1","0.5","0.9"]
- Parameters:
forecastTypes
- The forecast types used during predictor training. Default value is["0.1","0.5","0.9"]
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
forecastTypes
The forecast types used during predictor training. Default value is
["0.1","0.5","0.9"]
- Parameters:
forecastTypes
- The forecast types used during predictor training. Default value is["0.1","0.5","0.9"]
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
performAutoML
Whether the predictor is set to perform AutoML.
- Parameters:
performAutoML
- Whether the predictor is set to perform AutoML.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.The AutoML strategy used to train the predictor. Unless
LatencyOptimized
is specified, the AutoML strategy optimizes predictor accuracy.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.The AutoML strategy used to train the predictor. Unless
LatencyOptimized
is specified, the AutoML strategy optimizes predictor accuracy.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
DescribePredictorResponse.Builder autoMLOverrideStrategy(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.The AutoML strategy used to train the predictor. Unless
LatencyOptimized
is specified, the AutoML strategy optimizes predictor accuracy.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.The AutoML strategy used to train the predictor. Unless
LatencyOptimized
is specified, the AutoML strategy optimizes predictor accuracy.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:
-
performHPO
Whether the predictor is set to perform hyperparameter optimization (HPO).
- Parameters:
performHPO
- Whether the predictor is set to perform hyperparameter optimization (HPO).- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
trainingParameters
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.
- Parameters:
trainingParameters
- 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:
- Returns a reference to this object so that method calls can be chained together.
-
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.
-
evaluationParameters
default DescribePredictorResponse.Builder evaluationParameters(Consumer<EvaluationParameters.Builder> 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.
This is a convenience method that creates an instance of theEvaluationParameters.Builder
avoiding the need to create one manually viaEvaluationParameters.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toevaluationParameters(EvaluationParameters)
.- Parameters:
evaluationParameters
- a consumer that will call methods onEvaluationParameters.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
hpoConfig
The hyperparameter override values for the algorithm.
- Parameters:
hpoConfig
- The hyperparameter override values for the algorithm.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
hpoConfig
default DescribePredictorResponse.Builder hpoConfig(Consumer<HyperParameterTuningJobConfig.Builder> hpoConfig) The hyperparameter override values for the algorithm.
This is a convenience method that creates an instance of theHyperParameterTuningJobConfig.Builder
avoiding the need to create one manually viaHyperParameterTuningJobConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tohpoConfig(HyperParameterTuningJobConfig)
.- Parameters:
hpoConfig
- a consumer that will call methods onHyperParameterTuningJobConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
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.
-
inputDataConfig
default DescribePredictorResponse.Builder inputDataConfig(Consumer<InputDataConfig.Builder> inputDataConfig) Describes the dataset group that contains the data to use to train the predictor.
This is a convenience method that creates an instance of theInputDataConfig.Builder
avoiding the need to create one manually viaInputDataConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toinputDataConfig(InputDataConfig)
.- Parameters:
inputDataConfig
- a consumer that will call methods onInputDataConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
featurizationConfig
The featurization configuration.
- Parameters:
featurizationConfig
- The featurization configuration.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
featurizationConfig
default DescribePredictorResponse.Builder featurizationConfig(Consumer<FeaturizationConfig.Builder> featurizationConfig) The featurization configuration.
This is a convenience method that creates an instance of theFeaturizationConfig.Builder
avoiding the need to create one manually viaFeaturizationConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed tofeaturizationConfig(FeaturizationConfig)
.- Parameters:
featurizationConfig
- a consumer that will call methods onFeaturizationConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
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.
-
encryptionConfig
default DescribePredictorResponse.Builder encryptionConfig(Consumer<EncryptionConfig.Builder> encryptionConfig) An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
This is a convenience method that creates an instance of theEncryptionConfig.Builder
avoiding the need to create one manually viaEncryptionConfig.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed toencryptionConfig(EncryptionConfig)
.- Parameters:
encryptionConfig
- a consumer that will call methods onEncryptionConfig.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
predictorExecutionDetails
DescribePredictorResponse.Builder predictorExecutionDetails(PredictorExecutionDetails 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.
- Parameters:
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:
- Returns a reference to this object so that method calls can be chained together.
-
predictorExecutionDetails
default DescribePredictorResponse.Builder predictorExecutionDetails(Consumer<PredictorExecutionDetails.Builder> 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.
This is a convenience method that creates an instance of thePredictorExecutionDetails.Builder
avoiding the need to create one manually viaPredictorExecutionDetails.builder()
.When the
Consumer
completes,SdkBuilder.build()
is called immediately and its result is passed topredictorExecutionDetails(PredictorExecutionDetails)
.- Parameters:
predictorExecutionDetails
- a consumer that will call methods onPredictorExecutionDetails.Builder
- Returns:
- Returns a reference to this object so that method calls can be chained together.
- See Also:
-
estimatedTimeRemainingInMinutes
DescribePredictorResponse.Builder estimatedTimeRemainingInMinutes(Long estimatedTimeRemainingInMinutes) The estimated time remaining in minutes for the predictor training job to complete.
- Parameters:
estimatedTimeRemainingInMinutes
- The estimated time remaining in minutes for the predictor training job to complete.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
isAutoPredictor
Whether the predictor was created with CreateAutoPredictor.
- Parameters:
isAutoPredictor
- Whether the predictor was created with CreateAutoPredictor.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
datasetImportJobArns
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
- Parameters:
datasetImportJobArns
- An array of the ARNs of the dataset import jobs used to import training data for the predictor.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
datasetImportJobArns
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
- Parameters:
datasetImportJobArns
- An array of the ARNs of the dataset import jobs used to import training data for the predictor.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
status
The status of the predictor. States include:
-
ACTIVE
-
CREATE_PENDING
,CREATE_IN_PROGRESS
,CREATE_FAILED
-
DELETE_PENDING
,DELETE_IN_PROGRESS
,DELETE_FAILED
-
CREATE_STOPPING
,CREATE_STOPPED
The
Status
of the predictor must beACTIVE
before you can use the predictor to create a forecast.- Parameters:
status
- The status of the predictor. States include:-
ACTIVE
-
CREATE_PENDING
,CREATE_IN_PROGRESS
,CREATE_FAILED
-
DELETE_PENDING
,DELETE_IN_PROGRESS
,DELETE_FAILED
-
CREATE_STOPPING
,CREATE_STOPPED
The
Status
of the predictor must beACTIVE
before you can use the predictor to create a forecast.-
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
-
message
If an error occurred, an informational message about the error.
- Parameters:
message
- If an error occurred, an informational message about the error.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
creationTime
When the model training task was created.
- Parameters:
creationTime
- When the model training task was created.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
lastModificationTime
The last time the resource was modified. The timestamp depends on the status of the job:
-
CREATE_PENDING
- TheCreationTime
. -
CREATE_IN_PROGRESS
- The current timestamp. -
CREATE_STOPPING
- The current timestamp. -
CREATE_STOPPED
- When the job stopped. -
ACTIVE
orCREATE_FAILED
- When the job finished or failed.
- Parameters:
lastModificationTime
- The last time the resource was modified. The timestamp depends on the status of the job:-
CREATE_PENDING
- TheCreationTime
. -
CREATE_IN_PROGRESS
- The current timestamp. -
CREATE_STOPPING
- The current timestamp. -
CREATE_STOPPED
- When the job stopped. -
ACTIVE
orCREATE_FAILED
- When the job finished or failed.
-
- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
-
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
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:
-