software.amazon.awssdk.services.sagemaker.model

Interface TabularJobConfig.Builder

All Superinterfaces:

Buildable, CopyableBuilder<TabularJobConfig.Builder,TabularJobConfig>, SdkBuilder<TabularJobConfig.Builder,TabularJobConfig>, SdkPojo

Enclosing class:

TabularJobConfig

public static interface TabularJobConfig.Builder
extends SdkPojo, CopyableBuilder<TabularJobConfig.Builder,TabularJobConfig>

Method Summary

Modifier and Type	Method and Description
TabularJobConfig.Builder	candidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig) The configuration information of how model candidates are generated.
default TabularJobConfig.Builder	candidateGenerationConfig(Consumer<CandidateGenerationConfig.Builder> candidateGenerationConfig) The configuration information of how model candidates are generated.
TabularJobConfig.Builder	completionCriteria(AutoMLJobCompletionCriteria completionCriteria) Sets the value of the CompletionCriteria property for this object.
default TabularJobConfig.Builder	completionCriteria(Consumer<AutoMLJobCompletionCriteria.Builder> completionCriteria) Sets the value of the CompletionCriteria property for this object.
TabularJobConfig.Builder	featureSpecificationS3Uri(String featureSpecificationS3Uri) A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2.
TabularJobConfig.Builder	generateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
TabularJobConfig.Builder	mode(AutoMLMode mode) The method that Autopilot uses to train the data.
TabularJobConfig.Builder	mode(String mode) The method that Autopilot uses to train the data.
TabularJobConfig.Builder	problemType(ProblemType problemType) The type of supervised learning problem available for the model candidates of the AutoML job V2.
TabularJobConfig.Builder	problemType(String problemType) The type of supervised learning problem available for the model candidates of the AutoML job V2.
TabularJobConfig.Builder	sampleWeightAttributeName(String sampleWeightAttributeName) If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model.
TabularJobConfig.Builder	targetAttributeName(String targetAttributeName) The name of the target variable in supervised learning, usually represented by 'y'.

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

equalsBySdkFields, sdkFields

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

copy

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

applyMutation, build

Method Detail

candidateGenerationConfig

TabularJobConfig.Builder candidateGenerationConfig(CandidateGenerationConfig candidateGenerationConfig)

The configuration information of how model candidates are generated.

Parameters:

candidateGenerationConfig - The configuration information of how model candidates are generated.

Returns:

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

candidateGenerationConfig

default TabularJobConfig.Builder candidateGenerationConfig(Consumer<CandidateGenerationConfig.Builder> candidateGenerationConfig)

The configuration information of how model candidates are generated.

This is a convenience method that creates an instance of the CandidateGenerationConfig.Builder avoiding the need to create one manually via CandidateGenerationConfig.builder().

When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to candidateGenerationConfig(CandidateGenerationConfig).

Parameters:

candidateGenerationConfig - a consumer that will call methods on CandidateGenerationConfig.Builder

Returns:

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

See Also:

candidateGenerationConfig(CandidateGenerationConfig)

completionCriteria

TabularJobConfig.Builder completionCriteria(AutoMLJobCompletionCriteria completionCriteria)

Sets the value of the CompletionCriteria property for this object.

Parameters:

completionCriteria - The new value for the CompletionCriteria property for this object.

Returns:

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

completionCriteria

default TabularJobConfig.Builder completionCriteria(Consumer<AutoMLJobCompletionCriteria.Builder> completionCriteria)

Sets the value of the CompletionCriteria property for this object. This is a convenience method that creates an instance of the AutoMLJobCompletionCriteria.Builder avoiding the need to create one manually via AutoMLJobCompletionCriteria.builder().

When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to completionCriteria(AutoMLJobCompletionCriteria).

Parameters:

completionCriteria - a consumer that will call methods on AutoMLJobCompletionCriteria.Builder

Returns:

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

See Also:

completionCriteria(AutoMLJobCompletionCriteria)

featureSpecificationS3Uri

TabularJobConfig.Builder featureSpecificationS3Uri(String featureSpecificationS3Uri)

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

Parameters:

featureSpecificationS3Uri - A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

Returns:

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

mode

TabularJobConfig.Builder mode(String mode)

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Parameters:

mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Returns:

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

See Also:

AutoMLMode, AutoMLMode

mode

TabularJobConfig.Builder mode(AutoMLMode mode)

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Parameters:

mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Returns:

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

See Also:

AutoMLMode, AutoMLMode

generateCandidateDefinitionsOnly

TabularJobConfig.Builder generateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Parameters:

generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

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

problemType

TabularJobConfig.Builder problemType(String problemType)

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Parameters:

problemType - The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Returns:

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

See Also:

ProblemType, ProblemType

problemType

TabularJobConfig.Builder problemType(ProblemType problemType)

The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Parameters:

problemType - The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types.

You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

Returns:

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

See Also:

ProblemType, ProblemType

targetAttributeName

TabularJobConfig.Builder targetAttributeName(String targetAttributeName)

The name of the target variable in supervised learning, usually represented by 'y'.

Parameters:

targetAttributeName - The name of the target variable in supervised learning, usually represented by 'y'.

Returns:

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

sampleWeightAttributeName

TabularJobConfig.Builder sampleWeightAttributeName(String sampleWeightAttributeName)

If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Parameters:

sampleWeightAttributeName - If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

Support for sample weights is available in Ensembling mode only.

Returns:

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