https://github.com/392781/scikit-ntk

Neural Tangent Kernel (NTK) module for the scikit-learn library
https://github.com/392781/scikit-ntk

kernel-methods machine-learning neural-tangent-kernel ntk scikit scikit-learn

Last synced: 24 days ago
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Neural Tangent Kernel (NTK) module for the scikit-learn library

• Host: GitHub
• URL: https://github.com/392781/scikit-ntk
• Owner: 392781
• License: bsd-3-clause
• Created: 2022-06-19T20:02:54.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-10-07T23:05:29.000Z (29 days ago)
• Last Synced: 2024-10-07T23:09:15.276Z (29 days ago)
• Topics: kernel-methods, machine-learning, neural-tangent-kernel, ntk, scikit, scikit-learn
• Language: Python
• Homepage: https://pypi.org/project/scikit-ntk/
• Size: 53.7 KB
• Stars: 21
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Neural Tangent Kernel for `scikit-learn` Gaussian Processes

![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/392781/scikit-ntk/CI.yml?branch=master&label=Lint%2C%20Build%2C%20Install%2C%20Test&style=flat-square) ![PyPI](https://img.shields.io/pypi/v/scikit-ntk?style=flat-square) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/scikit-ntk?style=flat-square) ![PyPI - Downloads](https://img.shields.io/pypi/dm/scikit-ntk?style=flat-square) [![Bibtex citation](https://img.shields.io/badge/bibtex-citation-lightgrey?style=flat-square)](#citation)

**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" master's thesis (found at [http://hdl.handle.net/20.500.12680/d504rr81v](http://hdl.handle.net/20.500.12680/d504rr81v) and [https://arxiv.org/abs/2208.03761](https://arxiv.org/abs/2208.03761)).  This library is meant to directly integrate with [`sklearn.gaussian_process`](https://scikit-learn.org/stable/modules/classes.html#module-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 (>=3.8)

* scikit-learn (>=1.0.1)

### User installation

In terminal using `pip` run:

```bash

pip install scikit-ntk

```

### Usage

Usage is described in [`examples/usage.py`](https://github.com/392781/scikit-ntk/blob/master/example/usage.py); however, to get started simply import the `NeuralTangentKernel` class:

```py

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.

## Building

Python Poetry (>=1.2) is required if you wish to build `scikit-ntk` from source.  In order to build follow these steps:

1. Clone the repository

```bash

git clone [email protected]:392781/scikit-ntk.git

```

2. Enable a Poetry virtual environment

```bash

poetry shell

```

3. Build and install

```bash

poetry build

poetry install --with dev

```

## Citation

If you use scikit-ntk in your scientific work, please use the following citation alongside the scikit-learn citations found at [https://scikit-learn.org/stable/about.html#citing-scikit-learn](https://scikit-learn.org/stable/about.html#citing-scikit-learn):

```

@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    = {\url{http://hdl.handle.net/20.500.12680/d504rr81v}}

}

```