Struct DistributedGraphLearning 

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

Distributed Graph Learning for Large-Scale Semi-Supervised Learning

This method distributes graph learning across multiple workers to handle large-scale datasets that cannot fit in memory on a single machine. It uses a master-worker architecture with graph partitioning.

Parameters

• n_workers - Number of workers for distributed computation
• lambda_sparse - Sparsity regularization parameter
• beta_smoothness - Smoothness regularization parameter
• max_iter - Maximum number of iterations
• tol - Convergence tolerance
• partition_strategy - Strategy for graph partitioning (“random”, “metis”, “spectral”)
• communication_rounds - Number of communication rounds between workers

Examples

use scirs2_core::array;
use sklears_semi_supervised::DistributedGraphLearning;
use sklears_core::traits::{Predict, Fit};


let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0]];
let y = array![0, 1, -1, -1]; // -1 indicates unlabeled

let dgl = DistributedGraphLearning::new()
    .n_workers(2)
    .lambda_sparse(0.1)
    .partition_strategy("spectral".to_string());
let fitted = dgl.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl DistributedGraphLearning<Untrained>

pub fn new() -> Self

Create a new DistributedGraphLearning instance

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

Set the number of workers

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

Set the sparsity regularization parameter

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

Set the smoothness regularization parameter

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

Set the maximum number of iterations

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

Set the convergence tolerance

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

Set the learning rate

pub fn partition_strategy(self, strategy: String) -> Self

Set the graph partitioning strategy

pub fn communication_rounds(self, rounds: usize) -> Self

Set the number of communication rounds

pub fn overlap_ratio(self, ratio: f64) -> Self

Set the overlap ratio between partitions

pub fn consensus_weight(self, weight: f64) -> Self

Set the consensus weight for combining worker results

Trait Implementations

impl<S: Clone> Clone for DistributedGraphLearning<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for DistributedGraphLearning<S>

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

Formats the value using the given formatter.

impl Default for DistributedGraphLearning<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for DistributedGraphLearning<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]>>, ArrayBase<ViewRepr<&i32>, Dim<[usize; 1]>>> for DistributedGraphLearning<Untrained>

type Fitted = DistributedGraphLearning<DistributedGraphLearningTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &ArrayView1<'_, i32>, ) -> 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]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for DistributedGraphLearning<DistributedGraphLearningTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<i32>>

Make predictions on the provided data

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

Make predictions with confidence intervals

impl PredictProba<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for DistributedGraphLearning<DistributedGraphLearningTrained>

fn predict_proba(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>>

Predict class probabilities