"""Default preset for Gaussian process surrogates."""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from attrs import define
from baybe.kernels.basic import MaternKernel
from baybe.kernels.composite import ScaleKernel
from baybe.parameters import TaskParameter
from baybe.priors.basic import GammaPrior
from baybe.surrogates.gaussian_process.kernel_factory import KernelFactory
if TYPE_CHECKING:
from torch import Tensor
from baybe.kernels.base import Kernel
from baybe.searchspace.core import SearchSpace
# Boundaries for low and high dimension limits
_DIM_LIMITS = (8, 75)
[docs]
@define
class DefaultKernelFactory(KernelFactory):
"""A factory providing the default kernel for Gaussian process surrogates.
This is taking the low and high dimensional limits of
:class:`baybe.surrogates.gaussian_process.presets.edbo.EDBOKernelFactory`
and interpolates the prior moments linearly between them.
"""
def __call__( # noqa: D102
self, searchspace: SearchSpace, train_x: Tensor, train_y: Tensor
) -> Kernel:
# See base class.
effective_dims = train_x.shape[-1] - len(
[p for p in searchspace.parameters if isinstance(p, TaskParameter)]
)
# Interpolate prior moments linearly between low D and high D regime
# The high D regime itself is the average of the EDBO OHE and Mordred regime
# Values outside the dimension limits will get the border value assigned
lengthscale_prior = GammaPrior(
np.interp(effective_dims, _DIM_LIMITS, [1.2, 2.5]),
np.interp(effective_dims, _DIM_LIMITS, [1.1, 0.55]),
)
lengthscale_initial_value = np.interp(effective_dims, _DIM_LIMITS, [0.2, 6.0])
outputscale_prior = GammaPrior(
np.interp(effective_dims, _DIM_LIMITS, [5.0, 3.5]),
np.interp(effective_dims, _DIM_LIMITS, [0.5, 0.15]),
)
outputscale_initial_value = np.interp(effective_dims, _DIM_LIMITS, [8.0, 15.0])
return ScaleKernel(
MaternKernel(
nu=2.5,
lengthscale_prior=lengthscale_prior,
lengthscale_initial_value=lengthscale_initial_value,
),
outputscale_prior=outputscale_prior,
outputscale_initial_value=outputscale_initial_value,
)
def _default_noise_factory(
searchspace: SearchSpace, train_x: Tensor, train_y: Tensor
) -> tuple[GammaPrior, float]:
"""Create the default noise settings for the Gaussian process surrogate.
This is taking the low and high dimensional limits of
:func:`baybe.surrogates.gaussian_process.presets.edbo._edbo_noise_factory`
and interpolates the prior moments linearly between them.
"""
# TODO: Replace this function with a proper likelihood factory
# Interpolate prior moments linearly between low D and high D regime
# The high D regime itself is the average of the EDBO OHE and Mordred regime
# Values outside the dimension limits will get the border value assigned
effective_dims = train_x.shape[-1] - len(
[p for p in searchspace.parameters if isinstance(p, TaskParameter)]
)
return (
GammaPrior(
np.interp(effective_dims, _DIM_LIMITS, [1.05, 1.5]),
np.interp(effective_dims, _DIM_LIMITS, [0.5, 0.1]),
),
np.interp(effective_dims, _DIM_LIMITS, [0.1, 5.0]),
)