kernel.py

import numpy as np
from openbox.utils.util_funcs import get_types
from openbox.surrogate.base.gp import GaussianProcess
from openbox.surrogate.base.gp_base_prior import HorseshoePrior, LognormalPrior
from openbox.surrogate.base.gp_kernels import ConstantKernel, Matern, HammingKernel, WhiteKernel, RBF

def build_kernel(types,rng):

    cov_amp = ConstantKernel(
        2.0,
        constant_value_bounds=(np.exp(-10), np.exp(2)),
        prior=LognormalPrior(mean=0.0, sigma=1.0, rng=rng),
    )

    cont_dims = np.nonzero(types == 0)[0].astype(int)
    cat_dims = np.nonzero(types != 0)[0].astype(int)

    if len(cont_dims) > 0:
        exp_kernel = Matern(
            np.ones([len(cont_dims)]),
            [(np.exp(-6.754111155189306), np.exp(0.0858637988771976)) for _ in range(len(cont_dims))],
            nu=2.5,
            operate_on=cont_dims,
        )

    if len(cat_dims) > 0:
        ham_kernel = HammingKernel(
            np.ones([len(cat_dims)]),
            [(np.exp(-6.754111155189306), np.exp(0.0858637988771976)) for _ in range(len(cat_dims))],
            operate_on=cat_dims,
        )

    noise_kernel = WhiteKernel(
        noise_level=1e-8,
        noise_level_bounds=(np.exp(-25), np.exp(2)),
        prior=HorseshoePrior(scale=0.1, rng=rng),
    )

    if len(cont_dims) > 0 and len(cat_dims) > 0:
        # both
        kernel = cov_amp * (exp_kernel * ham_kernel) + noise_kernel
    elif len(cont_dims) > 0 and len(cat_dims) == 0:
        # only cont
        kernel = cov_amp * exp_kernel + noise_kernel
    elif len(cont_dims) == 0 and len(cat_dims) > 0:
        # only cont
        kernel = cov_amp * ham_kernel + noise_kernel
    else:
        raise ValueError()
    
    return kernel