Struct MultiLabelBinarizer 

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

MultiLabelBinarizer transforms between iterable of labels and binary matrix

Implementations

impl MultiLabelBinarizer<Untrained>

pub fn new() -> Self

Create a new MultiLabelBinarizer with default configuration

pub fn classes(self, classes: Vec<String>) -> Self

Set the classes to use

impl MultiLabelBinarizer<Trained>

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

Get the classes

pub fn inverse_transform(&self, y: &Array2<Float>) -> Result<Vec<Vec<String>>>

Transform binary matrix back to multi-label format

Trait Implementations

impl Default for MultiLabelBinarizer<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MultiLabelBinarizer<Untrained>

type Config = MultiLabelBinarizerConfig

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 Estimator for MultiLabelBinarizer<Trained>

type Config = MultiLabelBinarizerConfig

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<Vec<Vec<String>>, ()> for MultiLabelBinarizer<Untrained>

type Fitted = MultiLabelBinarizer<Trained>

The fitted model type

fn fit(self, y: &Vec<Vec<String>>, _x: &()) -> 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<Vec<Vec<String>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MultiLabelBinarizer<Trained>

fn transform(&self, y: &Vec<Vec<String>>) -> Result<Array2<Float>>

Transform the input data