sklears-semi-supervised

Latest release: 0.1.0-beta.1 (January 1, 2026). See the workspace release notes for highlights and upgrade guidance.

Overview

sklears-semi-supervised implements semi-supervised learning algorithms that align with scikit-learn’s API, covering label propagation, self-training, and graph-based methods.

Key Features

• Algorithms: LabelPropagation, LabelSpreading, SelfTrainingClassifier, CoTraining prototypes, and graph-based methods.
• Graph Support: Efficient knn graph construction, similarity kernels, and CUDA/WebGPU backends for large graphs.
• Pipeline Integration: Works with datasets containing missing labels and plugs into sklears pipelines.
• Monitoring: Built-in tracking for convergence diagnostics and label confidence scores.

Quick Start

use sklears_semi_supervised::LabelSpreading;
use scirs2_core::ndarray::{array, Array1};

let x = array![
    [0.0, 1.0],
    [1.0, 0.0],
    [1.0, 1.0],
    [0.5, 0.2],
];
let y = Array1::from(vec![0, 1, -1, -1]); // -1 denotes unlabeled

let model = LabelSpreading::builder()
    .kernel("rbf")
    .gamma(0.5)
    .max_iter(100)
    .tol(1e-3)
    .build();

let fitted = model.fit(&x, &y)?;
let inferred = fitted.transduced_labels();

Status

• Exercised by the shared 11,292 passing workspace tests for 0.1.0-beta.1.
• Delivers >99% parity with scikit-learn’s semi-supervised module, plus GPU graph acceleration.
• Additional experiments (semi-supervised regression, curriculum learning) tracked in TODO.md.