Struct MissingIndicator 

pub struct MissingIndicator<S = Untrained> { /* private fields */ }

Missing Indicator

Binary indicator for missing values.

Parameters

• missing_values - The placeholder for missing values (NaN by default)
• features - Which features to generate indicators for (‘missing-only’ or ‘all’)
• sparse - Whether to return sparse indicators
• error_on_new - Whether to raise an error when a new feature is completely missing during transform

Examples

use sklears_impute::MissingIndicator;
use sklears_core::traits::{Transform, Fit};
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [f64::NAN, 3.0], [7.0, 6.0]];

let indicator = MissingIndicator::new();
let fitted = indicator.fit(&X.view(), &()).unwrap();
let indicators = fitted.transform(&X.view()).unwrap();

Implementations

impl MissingIndicator<Untrained>

pub fn new() -> Self

Create a new MissingIndicator instance

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

Set the missing values placeholder

pub fn features(self, features: String) -> Self

Set which features to generate indicators for

pub fn sparse(self, sparse: bool) -> Self

Set whether to return sparse indicators

pub fn error_on_new(self, error_on_new: bool) -> Self

Set whether to raise an error on new missing features

Trait Implementations

impl<S: Clone> Clone for MissingIndicator<S>

fn clone(&self) -> MissingIndicator<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MissingIndicator<S>

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

Formats the value using the given formatter.

impl Default for MissingIndicator<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MissingIndicator<Untrained>

type Config = ()

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<ViewRepr<&f64>, Dim<[usize; 2]>>, ()> for MissingIndicator<Untrained>

type Fitted = MissingIndicator<MissingIndicatorTrained>

The fitted model type

fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> 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 Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MissingIndicator<MissingIndicatorTrained>

fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Transform the input data