ptgp

A Gaussian process library for building GP models that solve real-world problems. Built on PyTensor’s symbolic graph and rewrite system, with PyMC priors and native optimizers.

ptgp ships exact GPs, sparse VFE, SVGP with minibatch training, and Fourier features; a full kernel library with composition and active_dims; non-Gaussian likelihoods; and a training toolbox with L-BFGS-B and Adam, per-parameter learning rates, staged optimization, and inducing-point initialization.

Quick install

pip install -e .

Requires PyTensor from main and PyMC >= 6.0. See the installation guide for details.

Quick example

import numpy as np
import pymc as pm
import pytensor.tensor as pt
import ptgp as pg

X = np.random.randn(200, 1)
y = np.sin(X.ravel()) + 0.1 * np.random.randn(200)
Z = np.linspace(-2, 2, 20)[:, None]

with pm.Model() as model:
    ls = pm.InverseGamma("ls", alpha=2.0, beta=1.0)
    eta = pm.Exponential("eta", lam=1.0)
    kernel = eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls)

    vp = pg.gp.init_variational_params(M=20)
    svgp = pg.gp.SVGP(
        kernel=kernel,
        likelihood=pg.likelihoods.Gaussian(sigma=0.1),
        inducing_variable=pg.inducing.Points(pt.as_tensor(Z)),
        variational_params=vp,
    )

See the example gallery for full end-to-end walkthroughs.