Struct RobustPreprocessor 

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

Comprehensive robust preprocessor

Implementations

impl RobustPreprocessor<Untrained>

pub fn new() -> Self

Create a new RobustPreprocessor with default configuration

pub fn conservative() -> Self

Create a conservative robust preprocessor

pub fn moderate() -> Self

Create a moderate robust preprocessor

pub fn aggressive() -> Self

Create an aggressive robust preprocessor

pub fn custom() -> Self

Create a custom robust preprocessor

pub fn config(self, config: RobustPreprocessorConfig) -> Self

Set the configuration

pub fn outlier_detection(self, enable: bool) -> Self

Enable or disable outlier detection

pub fn outlier_transformation(self, enable: bool) -> Self

Enable or disable outlier transformation

pub fn outlier_imputation(self, enable: bool) -> Self

Enable or disable outlier-aware imputation

pub fn robust_scaling(self, enable: bool) -> Self

Enable or disable robust scaling

pub fn detection_method(self, method: OutlierDetectionMethod) -> Self

Set the outlier detection method

pub fn transformation_method(self, method: OutlierTransformationMethod) -> Self

Set the transformation method

pub fn outlier_threshold(self, threshold: Float) -> Self

Set the outlier threshold

pub fn adaptive_thresholds(self, enable: bool) -> Self

Enable adaptive thresholds

pub fn contamination_rate(self, rate: Float) -> Self

Set the contamination rate

pub fn quantile_range(self, range: (Float, Float)) -> Self

Set the quantile range for robust scaling

pub fn with_centering(self, center: bool) -> Self

Set whether to center data in robust scaling

pub fn with_scaling(self, scale: bool) -> Self

Set whether to scale data in robust scaling

pub fn parallel(self, enable: bool) -> Self

Enable parallel processing

impl RobustPreprocessor<Trained>

pub fn n_features_in(&self) -> Option<usize>

Get the number of features seen during fit

pub fn preprocessing_stats(&self) -> Option<&RobustPreprocessingStats>

Get preprocessing statistics

pub fn outlier_detector(&self) -> Option<&OutlierDetector<Trained>>

Get outlier detector (if enabled)

pub fn outlier_transformer(&self) -> Option<&OutlierTransformer<Trained>>

Get outlier transformer (if enabled)

pub fn outlier_imputer(&self) -> Option<&OutlierAwareImputer>

Get outlier-aware imputer (if enabled)

pub fn robust_scaler(&self) -> Option<&RobustScaler>

Get robust scaler (if enabled)

pub fn preprocessing_report(&self) -> Result<String>

Generate a comprehensive preprocessing report

pub fn is_effective(&self) -> bool

Check if the preprocessing was effective

pub fn get_recommendations(&self) -> Vec<String>

Get recommendations for improving preprocessing

Trait Implementations

impl<State: Clone> Clone for RobustPreprocessor<State>

fn clone(&self) -> RobustPreprocessor<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for RobustPreprocessor<State>

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

Formats the value using the given formatter.

impl Default for RobustPreprocessor<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ()> for RobustPreprocessor<Untrained>

type Fitted = RobustPreprocessor<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _y: &()) -> Result<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 Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for RobustPreprocessor<Trained>

fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Transform the input data