Struct MlSyntheticDataParametersBuilder 

#[non_exhaustive]
pub struct MlSyntheticDataParametersBuilder { /* private fields */ }

A builder for MlSyntheticDataParameters.

Implementations

impl MlSyntheticDataParametersBuilder

pub fn epsilon(self, input: f64) -> Self

The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.

This field is required.

pub fn set_epsilon(self, input: Option<f64>) -> Self

The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.

pub fn get_epsilon(&self) -> &Option<f64>

The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.

pub fn max_membership_inference_attack_score(self, input: f64) -> Self

The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.

This field is required.

pub fn set_max_membership_inference_attack_score( self, input: Option<f64>, ) -> Self

The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.

pub fn get_max_membership_inference_attack_score(&self) -> &Option<f64>

The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.

pub fn column_classification(self, input: ColumnClassificationDetails) -> Self

Classification details for data columns that specify how each column should be treated during synthetic data generation.

This field is required.

pub fn set_column_classification( self, input: Option<ColumnClassificationDetails>, ) -> Self

Classification details for data columns that specify how each column should be treated during synthetic data generation.

pub fn get_column_classification(&self) -> &Option<ColumnClassificationDetails>

Classification details for data columns that specify how each column should be treated during synthetic data generation.

pub fn build(self) -> Result<MlSyntheticDataParameters, BuildError>

Consumes the builder and constructs a MlSyntheticDataParameters. This method will fail if any of the following fields are not set:

• epsilon
• max_membership_inference_attack_score

Trait Implementations

impl Clone for MlSyntheticDataParametersBuilder

fn clone(&self) -> MlSyntheticDataParametersBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MlSyntheticDataParametersBuilder

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

Formats the value using the given formatter.

impl Default for MlSyntheticDataParametersBuilder

fn default() -> MlSyntheticDataParametersBuilder

Returns the “default value” for a type.

impl PartialEq for MlSyntheticDataParametersBuilder

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