sklears-kernel-approximation

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

Overview

sklears-kernel-approximation houses fast kernel feature map transformers, enabling scalable kernel methods for large datasets. The implementations track the scikit-learn 1.5 API while exploiting Rust's parallelism and SIMD acceleration.

Key Features

• Random Feature Maps: RBFSampler, Nystroem, AdditiveChi2Sampler, SkewedChi2Sampler, and more.
• GPU Acceleration: Optional CUDA/WebGPU backends for massive random feature expansions.
• Pipeline Ready: Builders integrate with sklears pipelines, grid search, and calibration stages.
• Deterministic Testing: Extensive property-based and integration tests ensure reproducible embeddings.

Quick Start

use sklears_kernel_approximation::RBFSampler;
use scirs2_core::ndarray::Array2;

let features: Array2<f64> = // load your data
    Array2::zeros((1024, 32));

let transformer = RBFSampler::builder()
    .gamma(0.5)
    .n_components(4096)
    .random_state(Some(42))
    .build();

let mapped = transformer.fit_transform(&features)?;

Status

• Verified by the workspace-wide 11,292 passing tests in 0.1.0-beta.1.
• Benchmarked against scikit-learn to provide 10–30× faster random feature generation.
• Further roadmap tasks (e.g., online updates, streaming sampling) tracked in TODO.md.