Struct CoTraining 

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

Co-Training classifier for semi-supervised learning with multiple views

Co-training uses two different feature sets (views) to train two classifiers. Each classifier labels unlabeled examples for the other classifier to use.

Parameters

• view1_features - Indices of features for view 1
• view2_features - Indices of features for view 2
• p - Number of positive examples to add per iteration
• n - Number of negative examples to add per iteration
• max_iter - Maximum number of iterations
• verbose - Whether to print progress information

Examples

use sklears_semi_supervised::CoTraining;
use sklears_core::traits::{Predict, Fit};


let X = array![[1.0, 2.0, 3.0, 4.0], [2.0, 3.0, 4.0, 5.0],
               [3.0, 4.0, 5.0, 6.0], [4.0, 5.0, 6.0, 7.0]];
let y = array![0, 1, -1, -1]; // -1 indicates unlabeled

let ct = CoTraining::new()
    .view1_features(vec![0, 1])
    .view2_features(vec![2, 3])
    .p(1)
    .n(1)
    .max_iter(10);
let fitted = ct.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl CoTraining<Untrained>

pub fn new() -> Self

Create a new CoTraining instance

pub fn view1_features(self, features: Vec<usize>) -> Self

Set the features for view 1

pub fn view2_features(self, features: Vec<usize>) -> Self

Set the features for view 2

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

Set the number of positive examples to add per iteration

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

Set the number of negative examples to add per iteration

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

Set the maximum number of iterations

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

Set verbosity

pub fn confidence_threshold(self, threshold: f64) -> Self

Set confidence threshold

Trait Implementations

impl<S: Clone> Clone for CoTraining<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for CoTraining<S>

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

Formats the value using the given formatter.

impl Default for CoTraining<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for CoTraining<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<ViewRepr<&i32>, Dim<[usize; 1]>>> for CoTraining<Untrained>

type Fitted = CoTraining<CoTrainingTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &ArrayView1<'_, 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; 1]>>> for CoTraining<CoTrainingTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<i32>>

Make predictions on the provided data

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

Make predictions with confidence intervals