Struct TaskRelationshipLearning 

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

Task Relationship Learning for Multi-Task Learning

This method learns explicit relationships between tasks and uses this information to regularize the learning process. Tasks that are determined to be related are encouraged to have similar parameters.

Examples

use sklears_multioutput::regularization::TaskRelationshipLearning;
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 task_relationship = TaskRelationshipLearning::new()
    .relationship_strength(0.1)
    .similarity_threshold(0.5)
    .max_iter(1000);

Implementations

impl TaskRelationshipLearning<Untrained>

pub fn new() -> Self

Create a new TaskRelationshipLearning instance

pub fn relationship_strength(self, strength: Float) -> Self

Set relationship regularization strength

pub fn similarity_threshold(self, threshold: Float) -> Self

Set similarity threshold for relationships

pub fn base_alpha(self, alpha: Float) -> Self

Set base regularization strength

pub fn similarity_method(self, method: TaskSimilarityMethod) -> Self

Set task similarity method

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

Set maximum iterations

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

Set tolerance

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

Set learning rate

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

Set task outputs

Trait Implementations

impl<S: Clone> Clone for TaskRelationshipLearning<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for TaskRelationshipLearning<S>

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

Formats the value using the given formatter.

impl Default for TaskRelationshipLearning<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for TaskRelationshipLearning<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 TaskRelationshipLearning<Untrained>

type Fitted = TaskRelationshipLearning<TaskRelationshipLearningTrained>

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 TaskRelationshipLearning<TaskRelationshipLearningTrained>

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