aws_sdk_sagemaker::types

Struct CandidateGenerationConfig

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#[non_exhaustive]
pub struct CandidateGenerationConfig {
    pub algorithms_config: Option<Vec<AutoMlAlgorithmConfig>>,
}
Expand description

Stores the configuration information for how model candidates are generated using an AutoML job V2.

Fields (Non-exhaustive)§

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
§algorithms_config: 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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impl CandidateGenerationConfig

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pub fn algorithms_config(&self) -> &[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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .algorithms_config.is_none().

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impl CandidateGenerationConfig

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pub fn builder() -> CandidateGenerationConfigBuilder

Creates a new builder-style object to manufacture CandidateGenerationConfig.

Trait Implementations§

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impl Clone for CandidateGenerationConfig

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fn clone(&self) -> CandidateGenerationConfig

Returns a duplicate of the value. Read more
1.0.0 · Source§

fn clone_from(&mut self, source: &Self)

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

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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 PartialEq for CandidateGenerationConfig

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

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

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 CandidateGenerationConfig

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