Struct AdversarialEnsembleClassifier 

pub struct AdversarialEnsembleClassifier<State = Untrained> { /* private fields */ }

Adversarial ensemble classifier

Implementations

impl<State> AdversarialEnsembleClassifier<State>

pub fn new(config: AdversarialEnsembleConfig) -> Self

Create a new adversarial ensemble classifier

pub fn fgsm_training() -> Self

Create adversarial ensemble with FGSM training

pub fn pgd_training() -> Self

Create adversarial ensemble with PGD training

pub fn defensive_distillation() -> Self

Create adversarial ensemble with defensive distillation

pub fn diversity_maximization() -> Self

Create adversarial ensemble with diversity maximization

pub fn n_estimators(self, n_estimators: usize) -> Self

Builder method to configure number of estimators

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

Builder method to configure epsilon

pub fn adversarial_ratio(self, ratio: f64) -> Self

Builder method to configure adversarial ratio

pub fn random_state(self, seed: u64) -> Self

Builder method to configure random state

pub fn input_preprocessing(self, preprocessing: InputPreprocessing) -> Self

Builder method to configure input preprocessing

impl AdversarialEnsembleClassifier<Trained>

pub fn robustness_metrics(&self) -> &RobustnessMetrics

Get robustness metrics

pub fn predict_with_detection( &self, X: &Array2<f64>, ) -> SklResult<(Vec<usize>, Vec<bool>)>

Predict with adversarial detection

pub fn diversity_score(&self, X: &Array2<f64>) -> SklResult<f64>

Get ensemble diversity score

pub fn evaluate_robustness( &self, X: &Array2<f64>, y: &[usize], attack_method: AttackMethod, ) -> SklResult<f64>

Evaluate robustness against specific attack

Trait Implementations

impl Estimator for AdversarialEnsembleClassifier<Untrained>

type Config = AdversarialEnsembleConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, Vec<usize>> for AdversarialEnsembleClassifier<Untrained>

type Fitted = AdversarialEnsembleClassifier<Trained>

The fitted model type

fn fit(self, X: &Array2<f64>, y: &Vec<usize>) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, AdversarialPredictionResults> for AdversarialEnsembleClassifier<Trained>

fn predict(&self, X: &Array2<f64>) -> SklResult<AdversarialPredictionResults>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals