GPy/GPy/testing/test_svgp.py

import numpy as np
import GPy


class TestSVGP_nonconvex:
    """
    Inference in the SVGP with a student-T likelihood
    """

    def setup(self):
        X = np.linspace(0, 10, 100).reshape(-1, 1)
        Z = np.linspace(0, 10, 10).reshape(-1, 1)
        Y = np.sin(X) + np.random.randn(*X.shape) * 0.1
        Y[50] += 3

        lik = GPy.likelihoods.StudentT(deg_free=2)
        k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
        self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)

    def test_grad(self):
        self.setup()
        assert self.m.checkgrad(step=1e-4)


class TestSVGP_classification:
    """
    Inference in the SVGP with a Bernoulli likelihood
    """

    def setup(self):
        X = np.linspace(0, 10, 100).reshape(-1, 1)
        Z = np.linspace(0, 10, 10).reshape(-1, 1)
        Y = np.where((np.sin(X) + np.random.randn(*X.shape) * 0.1) > 0, 1, 0)

        lik = GPy.likelihoods.Bernoulli()
        k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
        self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k)

    def test_grad(self):
        self.setup()
        assert self.m.checkgrad(step=1e-4)


class TestSVGP_Poisson_with_meanfunction:
    """
    Inference in the SVGP with a Bernoulli likelihood
    """

    def setup(self):
        X = np.linspace(0, 10, 100).reshape(-1, 1)
        Z = np.linspace(0, 10, 10).reshape(-1, 1)
        latent_f = np.exp(0.1 * X * 0.05 * X**2)
        Y = np.array([np.random.poisson(f) for f in latent_f.flatten()]).reshape(-1, 1)

        mf = GPy.mappings.Linear(1, 1)

        lik = GPy.likelihoods.Poisson()
        k = GPy.kern.RBF(1, lengthscale=5.0) + GPy.kern.White(1, 1e-6)
        self.m = GPy.core.SVGP(X, Y, Z=Z, likelihood=lik, kernel=k, mean_function=mf)

    def test_grad(self):
        self.setup()
        assert self.m.checkgrad(step=1e-4)