Struct SupervisedContrastiveLearning 

pub struct SupervisedContrastiveLearning {
    pub embedding_dim: usize,
    pub temperature: f64,
    pub learning_rate: f64,
    pub batch_size: usize,
    pub max_epochs: usize,
    pub augmentation_strength: f64,
    pub labeled_weight: f64,
    pub random_state: Option<u64>,
}

Supervised Contrastive Learning for semi-supervised scenarios

This method extends contrastive learning to utilize both labeled and unlabeled data by pulling together samples from the same class while pushing apart samples from different classes.

Fields

embedding_dim: usize

embedding_dim

temperature: f64

temperature

learning_rate: f64

learning_rate

batch_size: usize

batch_size

max_epochs: usize

max_epochs

augmentation_strength: f64

augmentation_strength

labeled_weight: f64

labeled_weight

random_state: Option<u64>

random_state

Implementations

impl SupervisedContrastiveLearning

pub fn new() -> Self

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

pub fn temperature(self, temperature: f64) -> Result<Self>

pub fn learning_rate(self, learning_rate: f64) -> Self

pub fn batch_size(self, batch_size: usize) -> Result<Self>

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

pub fn augmentation_strength(self, augmentation_strength: f64) -> Result<Self>

pub fn labeled_weight(self, labeled_weight: f64) -> Self

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

Trait Implementations

impl Clone for SupervisedContrastiveLearning

fn clone(&self) -> SupervisedContrastiveLearning

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SupervisedContrastiveLearning

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

Formats the value using the given formatter.

impl Default for SupervisedContrastiveLearning

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for SupervisedContrastiveLearning

type Config = SupervisedContrastiveLearning

Configuration type for the estimator

type Error = ContrastiveLearningError

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 SupervisedContrastiveLearning

type Fitted = FittedSupervisedContrastiveLearning

The fitted model type

fn fit( self, X: &ArrayView2<'_, f64>, y: &ArrayView1<'_, i32>, ) -> 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