sklears-impute

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

Overview

sklears-impute provides data imputation algorithms and utilities that match scikit-learn’s impute module, with Rust-first performance improvements and extended functionality.

Key Features

• Imputers: SimpleImputer, KNNImputer, IterativeImputer, MissingIndicator, and multivariate extensions.
• Advanced Strategies: Matrix completion, expectation-maximization, GPU-accelerated KNN imputation.
• Pipelines: Drop-in compatibility with sklears pipelines and preprocessing workflows.
• Diagnostics: Missingness profiling, confidence intervals, and imputation quality metrics.

Quick Start

use sklears_impute::SimpleImputer;
use scirs2_core::ndarray::array;

let x = array![
    [1.0, f64::NAN, 2.0],
    [3.0, 4.0, f64::NAN],
    [f64::NAN, 6.0, 1.0],
];

let imputer = SimpleImputer::builder()
    .strategy("mean")
    .add_missing_value(f64::NAN)
    .build();

let imputed = imputer.fit_transform(&x)?;

Status

• Included in the 11,292 passing workspace tests for 0.1.0-beta.1.
• Supports dense and sparse matrices with deterministic output.
• Future tasks (streaming imputers, categorical encoders) tracked in this crate’s TODO.md.