Struct aws_sdk_sagemaker::types::AutoMlAlgorithmConfig

#[non_exhaustive]
pub struct AutoMlAlgorithmConfig {
    pub auto_ml_algorithms: Option<Vec<AutoMlAlgorithm>>,
}

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

Fields (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.

auto_ml_algorithms: Option<Vec<AutoMlAlgorithm>>

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Implementations

impl AutoMlAlgorithmConfig

pub fn auto_ml_algorithms(&self) -> &[AutoMlAlgorithm]

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

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

impl AutoMlAlgorithmConfig

pub fn builder() -> AutoMlAlgorithmConfigBuilder

Creates a new builder-style object to manufacture AutoMlAlgorithmConfig.

Trait Implementations

impl Clone for AutoMlAlgorithmConfig

fn clone(&self) -> AutoMlAlgorithmConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlAlgorithmConfig

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

Formats the value using the given formatter.

impl PartialEq for AutoMlAlgorithmConfig

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

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for AutoMlAlgorithmConfig