Struct MetaLearningPipeline 

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

Meta-learning pipeline component

Implementations

impl MetaLearningPipeline<Untrained>

pub fn new(base_estimator: Box<dyn PipelinePredictor>) -> Self

Create a new meta-learning pipeline

pub fn experience_storage(self, storage: ExperienceStorage) -> Self

Set the experience storage

pub fn adaptation_strategy(self, strategy: AdaptationStrategy) -> Self

Set the adaptation strategy

pub fn meta_parameters(self, params: HashMap<String, f64>) -> Self

Set meta-parameters

pub fn add_experience(&mut self, experience: Experience)

Add an experience to the storage

impl MetaLearningPipeline<MetaLearningPipelineTrained>

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

Predict using the fitted meta-learning pipeline

pub fn adapt_to_task( &mut self, task_id: String, x: &ArrayView2<'_, Float>, y: &ArrayView1<'_, Float>, ) -> SklResult<()>

Adapt to a new task with limited data

pub fn experience_storage(&self) -> &ExperienceStorage

Get the experience storage

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

Get the current meta-parameters

Trait Implementations

impl<S: Debug> Debug for MetaLearningPipeline<S>

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

Formats the value using the given formatter.

impl Estimator for MetaLearningPipeline<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 MetaLearningPipeline<Untrained>

type Fitted = MetaLearningPipeline<MetaLearningPipelineTrained>

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