This project builds upon GPTorch and PyTorch to construct a standard Gaussian Process Regression with Radial Basis Kernel likelihood.
Download the project to your local system, enter the download directory and then type
python setup.py install
if you want to install Scikit-GPR and its low-level modules in your Python distribution.
The following Python libraries will be required:
- scikit-learn
- PyTorch
- GPTorch
- numpy
With conda, you can create a complete environment with
conda env create -f environment.yml
import scikit_gpr.gaussian_process as gptorch
from sklearn.datasets import make_friedman2
X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
gpr = gptorch-GaussianProcessRegressorWithTorch(n_iter=200, normalize_y=True).fit(X, y)
gpr.score(X, y)
print(f"Observed: {y[:2]}\nPredicted: {gpr.predict(X[:2, :], return_std=True)}")You can type
help(gptorch.GaussianProcessRegressorWithTorch)
to understand better the inputs needed and the outputs returned
scikit-gpr: A scikit-learn regressor-like wrapper for Gaussian Process Regression using GPTorch
Copyright 2023 Hector Nieto.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.