Struct aws_sdk_sagemaker::types::builders::CandidateGenerationConfigBuilder

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#[non_exhaustive]
pub struct CandidateGenerationConfigBuilder { /* private fields */ }
Expand description

A builder for CandidateGenerationConfig.

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impl CandidateGenerationConfigBuilder

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pub fn algorithms_config(self, input: AutoMlAlgorithmConfig) -> Self

Appends an item to algorithms_config.

To override the contents of this collection use set_algorithms_config.

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

  • For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

    • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

    • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

      If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

    • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

    For the list of all algorithms per training mode, see AlgorithmConfig.

    For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

  • For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

    For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

    • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

      If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

    • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

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pub fn set_algorithms_config( self, input: Option<Vec<AutoMlAlgorithmConfig>> ) -> Self

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

  • For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

    • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

    • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

      If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

    • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

    For the list of all algorithms per training mode, see AlgorithmConfig.

    For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

  • For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

    For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

    • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

      If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

    • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

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pub fn get_algorithms_config(&self) -> &Option<Vec<AutoMlAlgorithmConfig>>

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

  • For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

    • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

    • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

      If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

    • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

    For the list of all algorithms per training mode, see AlgorithmConfig.

    For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

  • For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

    For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

    • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

      If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

    • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

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pub fn build(self) -> CandidateGenerationConfig

Consumes the builder and constructs a CandidateGenerationConfig.

Trait Implementations§

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impl Clone for CandidateGenerationConfigBuilder

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fn clone(&self) -> CandidateGenerationConfigBuilder

Returns a copy of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for CandidateGenerationConfigBuilder

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl Default for CandidateGenerationConfigBuilder

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fn default() -> CandidateGenerationConfigBuilder

Returns the “default value” for a type. Read more
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impl PartialEq for CandidateGenerationConfigBuilder

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fn eq(&self, other: &CandidateGenerationConfigBuilder) -> bool

This method tests for self and other values to be equal, and is used by ==.
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fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
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impl StructuralPartialEq for CandidateGenerationConfigBuilder

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