sklearn-jax-kernels 0.0.2

Composable kernels for scikit-learn implemented in JAX.

sklearn-jax-kernels

Warning: This project is still in an early stage it could be that the API will change in the future, further functionality is still very limited to the use cases which defined the creation of the project (application to DNA sequences present in Biology).

Why?

Ever wanted to run a kernel-based model from scikit-learn on a relatively large dataset? If so you will have noticed, that this can take extraordinarily long and require huge amounts of memory, especially if you are using compositions of kernels (such as for example k1 * k2 + k3). This is due to the way Kernels are computed in scikit-learn: For each kernel, the complete kernel matrix is computed, and the compositions are then computed from the kernel matrices. Further, scikit-learn does not rely on an automatic differentiation framework for the computation of gradients though kernel operations.

Introduction

sklearn-jax-kernels was designed to circumvent these issues:

• The utilization of JAX allows accelerating kernel computations through XLA optimizations, computation on GPUs and simplifies the computation of gradients though kernels
• The composition of kernels takes place on a per-element basis, such that unnecessary copies can be optimized away by JAX compilation

The goal of sklearn-jax-kernels is to provide the same flexibility and ease of use as known from scikit-learn kernels while improving speed and allowing the faster design of new kernels through Automatic Differentiation.

The kernels in this package follow the scikit-learn kernel API.

Quickstart

A short demonstration of how the kernels can be used, inspired by the scikit-learn documentation.

from sklearn import datasets
import jax.numpy as jnp
from sklearn_jax_kernels import RBF, GaussianProcessClassifier

iris = datasets.load_iris()
X = jnp.asarray(iris.data)
y = jnp.array(iris.target, dtype=int)

kernel = 1. + RBF(length_scale=1.0)
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)

Here a further example demonstrating how kernels can be combined:

from sklearn_jax_kernels.base_kernels import RBF, NormalizedKernel
from sklearn_jax_kernels.structured.strings import SpectrumKernel

my_kernel = RBF(1.) * SpectrumKernel(n_gram_length=3)
my_kernel_2 = RBF(1.) + RBF(2.)
my_kernel_2 = NormalizedKernel(my_kernel_2)

Some further inspiration can be taken from the tests in the subfolder tests.

Implemented Kernels

• Kernel compositions ($+,-,*,/$, exponentiation)
• Kernels for real valued data:
  • RBF kernel
• Kernels for same length strings:
  • SpectrumKernel
  • DistanceSpectrumKernel, SpectrumKernel with distance weight between matching substrings
  • ReverseComplement Spectrum kernel (relevant for applications in Biology when working with DNA sequences)

TODOs

• Implement more fundamental Kernels
• Implement jax compatible version of GaussianProcessRegressor
• Optimize GaussianProcessClassifier for performance
• Run benchmarks to show benefits in speed
• Add fake "split" kernel which allows to apply different kernels to different parts of the input