Struct aws_sdk_sagemaker::types::AutoMlCandidateGenerationConfig

#[non_exhaustive]
pub struct AutoMlCandidateGenerationConfig {
    pub feature_specification_s3_uri: Option<String>,
    pub algorithms_config: Option<Vec<AutoMlAlgorithmConfig>>,
}

Stores the configuration information for how a candidate is generated (optional).

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.

feature_specification_s3_uri: Option<String>

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. 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.

algorithms_config: Option<Vec<AutoMlAlgorithmConfig>>

Stores the configuration information for the selection of algorithms used to train the model candidates.

The list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

• AlgorithmsConfig should not be set in AUTO training mode.

• 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, AutoMLCandidateGenerationConfig uses the full set of algorithms for the given training mode.

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

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

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

Implementations

impl AutoMlCandidateGenerationConfig

pub fn feature_specification_s3_uri(&self) -> Option<&str>

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. 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.

pub fn algorithms_config(&self) -> Option<&[AutoMlAlgorithmConfig]>

Stores the configuration information for the selection of algorithms used to train the model candidates.

The list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

• AlgorithmsConfig should not be set in AUTO training mode.

• 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, AutoMLCandidateGenerationConfig uses the full set of algorithms for the given training mode.

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

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

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

impl AutoMlCandidateGenerationConfig

pub fn builder() -> AutoMlCandidateGenerationConfigBuilder

Creates a new builder-style object to manufacture AutoMlCandidateGenerationConfig.

Trait Implementations

impl Clone for AutoMlCandidateGenerationConfig

fn clone(&self) -> AutoMlCandidateGenerationConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlCandidateGenerationConfig

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

Formats the value using the given formatter.

impl PartialEq<AutoMlCandidateGenerationConfig> for AutoMlCandidateGenerationConfig

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

This method tests for self and other values to be equal, and is used by ==.

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.

impl StructuralPartialEq for AutoMlCandidateGenerationConfig