kernax-ml 0.1.4a0

A JAX-based kernel library for Gaussian Processes with automatic differentiation and composable operations

Kernax

A JAX-based kernel library for Gaussian Processes with automatic differentiation, JIT compilation, and composable kernel operations.

⚠️ Project Status: Kernax is in early development. The API may change, and some features are still experimental.

Features

• Fast JIT-compiled computations using JAX's @jit decorator
• Automatic dimension handling for scalars, vectors, matrices, and batched operations
• NaN-aware computations for working with padded/masked data
• Composable kernels through operator overloading (+, *, -)
• Distinct hyperparameters per batch for efficient multi-task learning
• PyTree integration for seamless use with JAX transformations (grad, vmap, etc.)

Installation

Install from PyPI:

pip install kernax-ml

Or clone the repository for development:

git clone https://github.com/SimLej18/kernax-ml
cd kernax-ml

Requirements:

• Python >= 3.14
• JAX >= 0.8.0

Using Conda (recommended):

conda create -n kernax-ml python=3.14
conda activate kernax-ml
pip install -e .

Using pip:

pip install -e .

Quick Start

import jax.numpy as jnp
from kernax import SEKernel, LinearKernel, DiagKernel, ExpKernel, BatchKernel, ARDKernel

# Create a simple Squared Exponential kernel
kernel = SEKernel(length_scale=1.0)

# Compute covariance between two points
x1 = jnp.array([1.0, 2.0])
x2 = jnp.array([1.5, 2.5])
cov = kernel(x1, x2)

# Compute covariance matrix for a set of points
X = jnp.array([[1.0], [2.0], [3.0]])
K = kernel(X, X)  # Returns 3x3 covariance matrix

# Compose kernels using operators
composite_kernel = SEKernel(length_scale=1.0) + DiagKernel(ExpKernel(0.1))  # SE + noise

# Use BatchKernel for distinct hyperparameters per batch
base_kernel = SEKernel(length_scale=1.0)
batched_kernel = BatchKernel(base_kernel, batch_size=10, batch_in_axes=0, batch_over_inputs=True)

# Use ARDKernel for Automatic Relevance Determination
length_scales = jnp.array([1.0, 2.0, 0.5])  # Different scale per dimension
ard_kernel = ARDKernel(SEKernel(length_scale=1.0), length_scales=length_scales)

Available Kernels

Base Kernels

• SEKernel (Squared Exponential, aka RBF or Gaussian)

  • Hyperparameters: length_scale

• LinearKernel

  • Hyperparameters: variance_b, variance_v, offset_c

• MaternKernel family

  • Matern12Kernel (ν=1/2, equivalent to Exponential)
  • Matern32Kernel (ν=3/2)
  • Matern52Kernel (ν=5/2)
  • Hyperparameters: length_scale

• PeriodicKernel

  • Hyperparameters: length_scale, variance, period

• RationalQuadraticKernel

  • Hyperparameters: length_scale, variance, alpha

• ConstantKernel

  • Hyperparameters: value

Composite Kernels

• SumKernel: Adds two kernels (use kernel1 + kernel2)
• ProductKernel: Multiplies two kernels (use kernel1 * kernel2)

Wrapper Kernels

Transform or modify kernel behavior:

• DiagKernel: Returns value only when inputs are equal (creates diagonal matrices)
• ExpKernel: Applies exponential to kernel output
• LogKernel: Applies logarithm to kernel output
• NegKernel: Negates kernel output (use -kernel)
• BatchKernel: Adds batch handling with distinct hyperparameters per batch
• BlockKernel: Constructs block covariance matrices for grouped data
• ActiveDimsKernel: Selects specific input dimensions before kernel computation
• ARDKernel: Applies Automatic Relevance Determination (different length scale per dimension)

Architecture

Kernax is built on Equinox, making kernels PyTorch-like modules with clean differentiation.

Each kernel uses a dual-class pattern:

1. Static Class (e.g., StaticSEKernel): Contains JIT-compiled computation logic
2. Instance Class (e.g., SEKernel): Extends eqx.Module, holds hyperparameters

This design enables:

• Efficient JIT compilation with Equinox's filter_jit
• Automatic PyTree registration through eqx.Module
• Seamless integration with JAX transformations (grad, vmap, etc.)
• Clean hyperparameter management with automatic array conversion

See CLAUDE.md for detailed architecture documentation.

Benchmarks

Kernax is designed for performance. Preliminary benchmarks show:

• Scalar operations: ~13-15 μs per covariance computation
• Matrix operations (10k × 15k): ~674-855 ms
• Batched operations (50 batches, 100×150): ~2.35-6.37 ms
• Composite kernels: Minimal overhead compared to base kernels

See benchmarks/ directory for detailed performance comparisons.

Development Status

✅ Completed

• Core kernel implementations (SE, Linear, Matern, Periodic, etc.)
• Kernel composition via operators
• Automatic dimension handling
• NaN-aware computations
• Equinox Module integration
• BatchKernel wrapper for batched hyperparameters
• ARDKernel wrapper for Automatic Relevance Determination
• ActiveDimsKernel wrapper for dimension selection

🚧 In Progress / Planned

• Rewrite inheritance with StationaryKernel and IsotropicKernel base classes
• Add computation engines for special cases (diagonal-only, etc.)
• Comprehensive test suite covering all new features
• Expanded documentation and tutorials
• PyPI package distribution
• Benchmarks against other libraries (GPJax, TinyGP, etc.)

Contributing

This project is in early development. Contributions, bug reports, and feature requests are welcome!

Related Projects

Kernax is developed alongside MagmaClust, a clustering and Gaussian Process library.

License

MIT License - see LICENSE file for details.

Citation

[Citation information to be added]