Struct StreamingGraphLearning 

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

Streaming Graph Learning for Dynamic Semi-Supervised Learning

This method continuously updates graph structures as new data points arrive, making it suitable for dynamic environments where the data distribution may change over time. It maintains a sliding window of recent data points and efficiently updates the graph structure and label propagation.

Parameters

• window_size - Size of the sliding window for maintaining recent data
• lambda_sparse - Sparsity regularization parameter for graph learning
• alpha_decay - Decay factor for edge weights over time
• update_frequency - Frequency of full graph reconstruction
• forgetting_factor - Factor for exponential forgetting of old connections
• adaptive_threshold - Whether to use adaptive thresholds for edge addition
• min_samples_update - Minimum samples required before updating the graph

Examples

use scirs2_core::array;
use sklears_semi_supervised::StreamingGraphLearning;
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 mut sgl = StreamingGraphLearning::new()
    .window_size(100)
    .lambda_sparse(0.1)
    .alpha_decay(0.95);

let mut fitted = sgl.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

// Update with new data
let X_new = array![[5.0, 6.0], [6.0, 7.0]];
let y_new = array![-1, 0];
let updated = fitted.update(&X_new.view(), &y_new.view()).unwrap();

Implementations

impl StreamingGraphLearning<Untrained>

pub fn new() -> Self

Create a new StreamingGraphLearning instance

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

Set the sliding window size

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

Set the sparsity regularization parameter

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

Set the decay factor for edge weights

pub fn update_frequency(self, frequency: usize) -> Self

Set the frequency of full graph reconstruction

pub fn forgetting_factor(self, factor: f64) -> Self

Set the forgetting factor for old connections

pub fn adaptive_threshold(self, adaptive: bool) -> Self

Enable/disable adaptive threshold for edge addition

pub fn min_samples_update(self, min_samples: usize) -> Self

Set minimum samples required before updating the graph

pub fn k_neighbors(self, k: usize) -> Self

Set the number of nearest neighbors to consider

pub fn similarity_threshold(self, threshold: f64) -> Self

Set the similarity threshold for edge creation

impl StreamingGraphLearning<StreamingGraphLearningTrained>

pub fn update( &mut self, X_new: &ArrayView2<'_, Float>, y_new: &ArrayView1<'_, i32>, ) -> SklResult<()>

Update the model with new streaming data

Trait Implementations

impl<S: Clone> Clone for StreamingGraphLearning<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for StreamingGraphLearning<S>

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

Formats the value using the given formatter.

impl Default for StreamingGraphLearning<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for StreamingGraphLearning<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 StreamingGraphLearning<Untrained>

type Fitted = StreamingGraphLearning<StreamingGraphLearningTrained>

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 StreamingGraphLearning<StreamingGraphLearningTrained>

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 StreamingGraphLearning<StreamingGraphLearningTrained>

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

Predict class probabilities