rustylab 0.1.0

RustyLab is a high‑performance scientific computing and machine learning library for Python, powered by a pure Rust core and exposed through PyO3.

🚧 RustyLab is under active development and its functionality is subject to change.

RustyLab is a high-performance scientific computing and machine learning library for Python, written entirely in Rust.

It aims to unify the core functionality of SciPy, statsmodels, and scikit-learn into a single, cohesive Python library with a strong focus on speed, safety, and ergonomics.

RustyLab is built in Rust and exposed to Python using PyO3 and maturin, combining Rust’s performance with Python’s ease of use.

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🚀 Why RustyLab?

The Python scientific ecosystem is powerful, but fragmented:

• Numerical routines live in SciPy;
• Statistical modelling in statsmodels;
• Machine learning in scikit-learn;
• Performance is built on a long-evolving foundation of native implementations and Python APIs.

RustyLab takes a different approach:

• Pure Rust core - memory safe, fast, and modern;
• Polars-native data model - designed for full compatibility with Polars;
• Unified API - statistics, optimization, and ML under one roof;
• Python bindings - no compromises in usability;
• Performance by default - zero-cost abstractions and parallelism;
• Safe & predictable - fewer runtime surprises.
• Auditable source – all function implementations are readily accessible, with Wiki references pointing to the exact code locations.

📦 Installation

The latest release of the Python client can be installed using pip.

pip install rustylab

📏 Numerical Accuracy

To ensure the highest level of numerical accuracy, all functions in RustyLab are rigorously tested using pytest with pytest.approx. The tests enforce strict tolerance thresholds:

• Relative tolerance (rel): 1e-12
• Absolute tolerance (abs): 1e-15

These values are chosen to align with the precision limits of f64 (double-precision) floating-point arithmetic, ensuring that:

1. Rounding errors are accounted for while maintaining high accuracy.
2. Edge cases (e.g., near-zero or very large values) are handled robustly.
3. Consistency with scientific computing standards (e.g., SciPy/NumPy) is maintained.

This approach guarantees that RustyLab’s algorithms deliver reliable and reproducible results across diverse applications, from general-purpose computations to high-precision scientific workloads.

📚 Documentation & Module Reference

RustyLab’s full API documentation is maintained in the project's Wiki, which includes:

• A structured overview of all available modules.
• Documentation strings for every public function.
• Direct links to the corresponding Rust source files.
• Usage examples.

Explore the documentation here:
👉 RustyLab Wiki — https://codeberg.org/esuriddick/rustylab/wiki

The Wiki is continuously updated as new features are added, making it the best place to understand the library’s capabilities.