Struct MetaLearningMultiTask 

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

Meta-Learning for Multi-Task Learning

This method learns meta-parameters that can quickly adapt to new tasks. It uses a model-agnostic meta-learning (MAML) approach adapted for multi-task scenarios.

Examples

use sklears_multioutput::regularization::MetaLearningMultiTask;
use sklears_core::traits::{Predict, Fit};
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;
use std::collections::HashMap;

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0], [4.0, 4.0]];
let mut y_tasks = HashMap::new();
y_tasks.insert("task1".to_string(), array![[1.0], [2.0], [1.5], [2.5]]);
y_tasks.insert("task2".to_string(), array![[0.5], [1.0], [0.8], [1.2]]);

let meta_learning = MetaLearningMultiTask::new()
    .meta_learning_rate(0.01)
    .inner_learning_rate(0.1)
    .n_inner_steps(5)
    .max_iter(1000);

Implementations

impl MetaLearningMultiTask<Untrained>

pub fn new() -> Self

Create a new MetaLearningMultiTask instance

pub fn meta_learning_rate(self, lr: Float) -> Self

Set meta-learning rate

pub fn inner_learning_rate(self, lr: Float) -> Self

Set inner learning rate

pub fn n_inner_steps(self, steps: usize) -> Self

Set number of inner gradient steps

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

Set maximum iterations

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

Set tolerance

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

Set random state

pub fn task_outputs(self, outputs: &[(&str, usize)]) -> Self

Set task outputs

impl MetaLearningMultiTask<MetaLearningMultiTaskTrained>

pub fn adapt_to_new_task( &self, X: &ArrayView2<'_, Float>, y: &Array2<Float>, n_adaptation_steps: usize, ) -> SklResult<(Array2<Float>, Array1<Float>)>

Adapt meta-parameters to a new task with few examples

pub fn get_meta_parameters(&self) -> (&Array2<Float>, &Array1<Float>)

Get meta-parameters for initialization of new tasks

Trait Implementations

impl<S: Clone> Clone for MetaLearningMultiTask<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MetaLearningMultiTask<S>

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

Formats the value using the given formatter.

impl Default for MetaLearningMultiTask<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MetaLearningMultiTask<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]>>, HashMap<String, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>>> for MetaLearningMultiTask<Untrained>

type Fitted = MetaLearningMultiTask<MetaLearningMultiTaskTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &HashMap<String, Array2<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

impl Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, HashMap<String, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>>> for MetaLearningMultiTask<MetaLearningMultiTaskTrained>

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

Make predictions on the provided data

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

Make predictions with confidence intervals