Struct KNNImputer 

pub struct KNNImputer { /* private fields */ }

K-Nearest Neighbors Imputer for filling missing values

This transformer fills missing values using k-nearest neighbors. For each sample, the missing features are imputed from the nearest neighbors that have a value for that feature.

Implementations

impl KNNImputer

pub fn new( _nneighbors: usize, metric: DistanceMetric, weights: WeightingScheme, missingvalues: f64, ) -> Self

Creates a new KNNImputer

Arguments

• _nneighbors - Number of neighboring samples to use for imputation
• metric - Distance metric for finding neighbors
• weights - Weight function used in imputation
• missingvalues - The value that represents missing data (default: NaN)

Returns

• A new KNNImputer instance

pub fn with_defaults() -> Self

Creates a KNNImputer with default parameters

Uses 5 neighbors, Euclidean distance, uniform weighting, and NaN as missing values

pub fn with_n_neighbors(_nneighbors: usize) -> Self

Creates a KNNImputer with specified number of neighbors and defaults for other parameters

pub fn with_distance_weighting(_nneighbors: usize) -> Self

Creates a KNNImputer with distance weighting

pub fn fit<S>(&mut self, x: &ArrayBase<S, Ix2>) -> Result<()>

where S: Data, S::Elem: Float + NumCast,

Fits the KNNImputer to the input data

Arguments

• x - The input data, shape (n_samples, n_features)

Returns

• Result<()> - Ok if successful, Err otherwise

pub fn transform<S>(&self, x: &ArrayBase<S, Ix2>) -> Result<Array2<f64>>

where S: Data, S::Elem: Float + NumCast,

Transforms the input data by imputing missing values

Arguments

• x - The input data, shape (n_samples, n_features)

Returns

• Result<Array2<f64>> - Transformed data with missing values imputed

pub fn fit_transform<S>(&mut self, x: &ArrayBase<S, Ix2>) -> Result<Array2<f64>>

where S: Data, S::Elem: Float + NumCast,

Fits the imputer and transforms the data in one step

Arguments

• x - The input data, shape (n_samples, n_features)

Returns

• Result<Array2<f64>> - Transformed data with missing values imputed

pub fn _nneighbors(&self) -> usize

Returns the number of neighbors used for imputation

pub fn metric(&self) -> &DistanceMetric

Returns the distance metric used

pub fn weights(&self) -> &WeightingScheme

Returns the weighting scheme used