scikit-ntk 1.0.0

Implementation of the neural tangent kernel for scikit-learn's Gaussian process module.

Neural Tangent Kernel for scikit-learn Gaussian Processes

scikit-ntk is implementation of the neural tangent kernel (NTK) for the scikit-learn machine learning library as part of "An Empirical Analysis of the Laplace and Neural Tangent Kernels" (https://arxiv.org/abs/2208.03761) master's thesis. This library is meant to directly integrate with sklearn.gaussian_process module. This implementation of the NTK can be used in combination with other kernels to train and predict with Gaussian process regressors and classifiers.

Installation

Dependencies

scikit-ntk requires:

• Python (>=3.7)
• scikit-learn (>=1.0.1)

User installation

In terminal using pip run:

pip install scikit-ntk

Usage

Useage is described in examples/usage.py; however, to get started simply import the NeuralTangentKernel class:

from skntk import NeuralTangentKernel as NTK

kernel_ntk = NTK(D=3, bias=0.01, bias_bounds=(1e-6, 1e6))

Once declared, usage is the same as other scikit-learn kernels.

Citation

If you use scikit-ntk in your scientific work, please use the following citation:

@mastersthesis{lencevicius2022laplacentk,
  author  = "Ronaldas Paulius Lencevicius",
  title   = "An Empirical Analysis of the Laplace and Neural Tangent Kernels",
  school  = "California State Polytechnic University, Pomona",
  year    = "2022",
  month   = "August",
  note    = "https://arxiv.org/abs/2208.03761"
}

along with the one listed on the scikit-learn website: https://scikit-learn.org/stable/about.html#citing-scikit-learn