sklears-isotonic

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

Overview

sklears-isotonic delivers isotonic regression utilities that mirror scikit-learn while taking advantage of Rust performance. The crate powers monotonic calibration, pairwise ranking, and constrained curve fitting across the wider sklears ecosystem.

Key Features

• Isotonic Regression: Fit monotonic functions for regression, probability calibration, and ranking tasks.
• Sparse + Dense Support: Optimized for both dense ndarray inputs and sparse CSR matrices.
• GPU-Ready Kernels: Optional CUDA/WebGPU acceleration for large calibration workloads.
• Pipeline Integration: Seamlessly composes with sklears preprocessing, model selection, and calibration APIs.

Quick Start

use sklears_isotonic::IsotonicRegression;
use scirs2_core::ndarray::{array, Array1};

let x = array![0.0, 1.0, 2.0, 3.0, 4.0];
let y = Array1::from(vec![0.1, 0.4, 0.3, 0.8, 0.9]);

let model = IsotonicRegression::builder()
    .increasing(true)
    .y_min(0.0)
    .y_max(1.0)
    .build();

let fitted = model.fit(&x, &y)?;
let predictions = fitted.predict(&x)?;

Status

• Validated via the 10,013-passing workspace test suite included with 0.1.0-beta.1.
• API surface aligns with scikit-learn 1.5 isotonic regression modules.
• Detailed roadmap items live in TODO.md within this crate.