Struct OneHotEncoder 

pub struct OneHotEncoder { /* private fields */ }

Encode categorical features as one-hot numeric columns, mirroring sklearn.preprocessing.OneHotEncoder. Categories per column are sorted lexicographically (sklearn default).

Input is a 2-D StrMatrix; output is a dense Matrix by default. Use sparse_output and transform_sparse for CSR output.

Implementations

impl OneHotEncoder

pub fn new() -> Self

Creates a new one-hot encoder.

pub fn drop(self, d: DropStrategy) -> Self

Sets the category drop strategy.

pub fn handle_unknown(self, h: HandleUnknown) -> Self

Sets how unknown categories are handled during transform.

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

Enable or disable sparse (CSR) output. When true, use transform_sparse to obtain a SparseMatrix. Default is false (dense).

pub fn is_sparse(&self) -> bool

Returns whether sparse output is enabled.

pub fn categories(&self) -> &[Vec<String>]

Returns the learned categories per column.

pub fn n_output_cols(&self) -> usize

Returns the number of output columns after transform.

pub fn fit(&mut self, x: &StrMatrix) -> Result<()>

Learns the sorted categories per column.

pub fn transform(&self, x: &StrMatrix) -> Result<Matrix>

Encodes the input as a dense one-hot matrix.

pub fn fit_transform(&mut self, x: &StrMatrix) -> Result<Matrix>

Fits the encoder and transforms the input into a dense matrix.

pub fn transform_sparse(&self, x: &StrMatrix) -> Result<SparseMatrix>

Transform using sparse (CSR) output, regardless of the sparse_output flag. Useful when only some callers need sparse data.

pub fn fit_transform_sparse(&mut self, x: &StrMatrix) -> Result<SparseMatrix>

Convenience: fit then transform into sparse output.

pub fn inverse_transform(&self, x: &Matrix) -> Result<StrMatrix>

Convert one-hot encoded dense data back to categorical strings.

For each row, the active (1.0) column within each feature block is decoded to its category. When drop = First, an all-zero block decodes to the dropped (first) category. All-zero blocks with drop = None decode to an empty string (matching sklearn’s None).

pub fn inverse_transform_sparse(&self, x: &SparseMatrix) -> Result<StrMatrix>

Inverse transform from sparse (CSR) input.

pub fn transform_auto(&self, x: &StrMatrix) -> Result<OneHotOutput>

Transform returning sparse if sparse_output is set, dense otherwise.

Trait Implementations

impl CategoricalTransformer for OneHotEncoder

fn name(&self) -> &'static str

Human-readable name of the transformer.

fn fit(&mut self, x: &StrMatrix) -> Result<()>

Fit the transformer on categorical training data.

fn transform(&self, x: &StrMatrix) -> Result<Matrix>

Transform categorical data using fitted parameters, returning a numeric Matrix.

fn inverse_transform(&self, y: &Matrix) -> Result<StrMatrix>

Reverse the transformation, recovering category strings from numeric codes. Not all encoders support this; the default implementation returns an error.

fn is_fitted(&self) -> bool

Whether the transformer has been fitted.

fn fit_transform(&mut self, x: &StrMatrix) -> Result<Matrix>

Convenience: fit then transform.

impl Clone for OneHotEncoder

fn clone(&self) -> OneHotEncoder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for OneHotEncoder

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

Formats the value using the given formatter.

impl Default for OneHotEncoder

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for OneHotEncoder

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Estimator for OneHotEncoder

impl FeatureNames for OneHotEncoder

fn feature_names_out(&self, input_features: Option<&[String]>) -> Vec<String>

Returns the output feature names given the optional input feature names.

impl Serialize for OneHotEncoder

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.