Struct TabularJobConfig

#[non_exhaustive]
pub struct TabularJobConfig {
    pub candidate_generation_config: Option<CandidateGenerationConfig>,
    pub completion_criteria: Option<AutoMlJobCompletionCriteria>,
    pub feature_specification_s3_uri: Option<String>,
    pub mode: Option<AutoMlMode>,
    pub generate_candidate_definitions_only: Option<bool>,
    pub problem_type: Option<ProblemType>,
    pub target_attribute_name: Option<String>,
    pub sample_weight_attribute_name: Option<String>,
}

The collection of settings used by an AutoML job V2 for the tabular problem type.

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.

candidate_generation_config: Option<CandidateGenerationConfig>

The configuration information of how model candidates are generated.

completion_criteria: Option<AutoMlJobCompletionCriteria>

How long a job is allowed to run, or how many candidates a job is allowed to generate.

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 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.

mode: Option<AutoMlMode>

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.

generate_candidate_definitions_only: Option<bool>

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

problem_type: Option<ProblemType>

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

target_attribute_name: Option<String>

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

sample_weight_attribute_name: Option<String>

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.

Implementations

impl TabularJobConfig

pub fn candidate_generation_config(&self) -> Option<&CandidateGenerationConfig>

The configuration information of how model candidates are generated.

pub fn completion_criteria(&self) -> Option<&AutoMlJobCompletionCriteria>

How long a job is allowed to run, or how many candidates a job is allowed to generate.

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 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.

pub fn mode(&self) -> Option<&AutoMlMode>

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.

pub fn generate_candidate_definitions_only(&self) -> Option<bool>

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

pub fn problem_type(&self) -> Option<&ProblemType>

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

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

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

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

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.

impl TabularJobConfig

pub fn builder() -> TabularJobConfigBuilder

Creates a new builder-style object to manufacture TabularJobConfig.

Trait Implementations

impl Clone for TabularJobConfig

fn clone(&self) -> TabularJobConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TabularJobConfig

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

Formats the value using the given formatter.

impl PartialEq for TabularJobConfig

fn eq(&self, other: &TabularJobConfig) -> 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 TabularJobConfig