Struct TransferLearningPipeline 

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

Transfer learning pipeline

Implementations

impl TransferLearningPipeline<Untrained>

pub fn new( pretrained_model: PretrainedModel, target_estimator: Box<dyn PipelinePredictor>, ) -> Self

Create a new transfer learning pipeline

pub fn transfer_strategy(self, strategy: TransferStrategy) -> Self

Set the transfer strategy

pub fn adaptation_config(self, config: AdaptationConfig) -> Self

Set the adaptation configuration

pub fn feature_extraction(pretrained_model: PretrainedModel) -> Self

Create a feature extraction pipeline

pub fn fine_tuning( pretrained_model: PretrainedModel, target_estimator: Box<dyn PipelinePredictor>, learning_rate: f64, epochs: usize, ) -> Self

Create a fine-tuning pipeline

pub fn knowledge_distillation( teacher_model: PretrainedModel, student_estimator: Box<dyn PipelinePredictor>, temperature: f64, distillation_weight: f64, task_weight: f64, ) -> Self

Create a knowledge distillation pipeline

impl TransferLearningPipeline<TransferLearningPipelineTrained>

pub fn predict(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array1<f64>>

Predict using the adapted model

pub fn adaptation_metrics(&self) -> &HashMap<String, f64>

Get the adaptation metrics

pub fn feature_extractor(&self) -> Option<&PretrainedModel>

Get the feature extractor

pub fn extract_features( &self, x: &ArrayView2<'_, Float>, ) -> SklResult<Array2<f64>>

Extract features using the pretrained model

pub fn fine_tune( &mut self, x: &ArrayView2<'_, Float>, y: &ArrayView1<'_, Float>, learning_rate: f64, epochs: usize, ) -> SklResult<()>

Fine-tune on new data

Trait Implementations

impl<S: Debug> Debug for TransferLearningPipeline<S>

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

Formats the value using the given formatter.

impl Estimator for TransferLearningPipeline<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]>>, Option<&ArrayBase<ViewRepr<&f64>, Dim<[usize; 1]>>>> for TransferLearningPipeline<Untrained>

type Fitted = TransferLearningPipeline<TransferLearningPipelineTrained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &Option<&ArrayView1<'_, Float>>, ) -> 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