I'm having a lot of difficulty fitting a simple 2-dimensional GP out-of-the box using GPyTorch. As you can see below, the fit is very poor, and does not improve much with either swapping out the RBF kernel for something like a Matern. The optimization does appear to converge, but not on anything sensible.
class GPRegressionModel(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood):
super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel(ard_num_dims=2),
)
def forward(self, x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
Does anyone have good tutorial examples beyond the ones included in the docs?