Struct BayesianClassifierChain 

pub struct BayesianClassifierChain<S = Untrained> {
    pub order: Option<Vec<usize>>,
    pub n_samples: usize,
    pub prior_strength: Float,
    pub random_state: Option<u64>,
    /* private fields */
}

Bayesian Classifier Chain

A probabilistic variant of classifier chain that uses Bayesian inference for the binary classifiers, providing uncertainty quantification alongside predictions.

Examples

use sklears_multioutput::chains::BayesianClassifierChain;
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

// This is a simple example showing the structure
let data = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0]];
let labels = array![[0, 1], [1, 0], [1, 1]];
let model = BayesianClassifierChain::new()
    .n_samples(100)
    .prior_strength(1.0);

Fields

order: Option<Vec<usize>>

order

n_samples: usize

n_samples

prior_strength: Float

prior_strength

random_state: Option<u64>

random_state

Implementations

impl BayesianClassifierChain<Untrained>

pub fn new() -> Self

Create a new BayesianClassifierChain instance

pub fn order(self, order: Vec<usize>) -> Self

Set the chain order

pub fn n_samples(self, n_samples: usize) -> Self

Set number of posterior samples

pub fn prior_strength(self, prior_strength: Float) -> Self

Set prior strength (regularization parameter)

pub fn random_state(self, random_state: u64) -> Self

Set random state for reproducibility

impl BayesianClassifierChain<Untrained>

pub fn fit_simple( self, X: &ArrayView2<'_, Float>, y: &Array2<i32>, ) -> SklResult<BayesianClassifierChain<BayesianClassifierChainTrained>>

Fit the Bayesian classifier chain

impl BayesianClassifierChain<BayesianClassifierChainTrained>

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

Predict with uncertainty quantification

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

Predict probabilities with Bayesian averaging

pub fn chain_order(&self) -> &[usize]

Get the chain order used during training

pub fn n_models(&self) -> usize

Get number of Bayesian models in the chain

pub fn model_posterior_stats( &self, model_idx: usize, ) -> Option<(&Array1<Float>, &Array2<Float>)>

Get posterior statistics for a specific model in the chain

pub fn order(&self) -> &[usize]

Get the chain order used during training

Trait Implementations

impl<S: Clone> Clone for BayesianClassifierChain<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for BayesianClassifierChain<S>

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

Formats the value using the given formatter.

impl Default for BayesianClassifierChain<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for BayesianClassifierChain<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]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>, BayesianClassifierChainTrained> for BayesianClassifierChain<Untrained>

type Fitted = BayesianClassifierChain<BayesianClassifierChainTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &Array2<i32>, ) -> 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 Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for BayesianClassifierChain<BayesianClassifierChainTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<i32>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals