Source code for smac.model.gaussian_process.kernels.base_kernels

from __future__ import annotations

from abc import abstractmethod
from typing import Any, Callable

from inspect import Signature, signature

import numpy as np
import sklearn.gaussian_process.kernels as kernels

from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
from smac.utils.configspace import get_conditional_hyperparameters

__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"


[docs]class AbstractKernel: """ This is a mixin for a kernel to override functions of the kernel. Because it overrides functions of the kernel, it needs to be placed first in the inheritance hierarchy. For this reason it is not possible to subclass the Mixin from the kernel class because this will prevent it from being instantiatable. Therefore, mypy won't know about anything related to the superclass and some type:ignore statements has to be added when accessing a member that is declared in the superclass such as `self.has_conditions`, `self._call`, `super().get_params`, etc. Parameters ---------- operate_on : np.ndarray, defaults to None On which numpy array should be operated on. has_conditions : bool, defaults to False Whether the kernel has conditions. prior : AbstractPrior, defaults to None Which prior the kernel is using. Attributes ---------- operate_on : np.ndarray, defaults to None On which numpy array should be operated on. has_conditions : bool, defaults to False Whether the kernel has conditions. Might be changed by the gaussian process. prior : AbstractPrior, defaults to None Which prior the kernel is using. Primarily used by sklearn. """ def __init__( self, *, operate_on: np.ndarray | None = None, has_conditions: bool = False, prior: AbstractPrior | None = None, **kwargs: Any, ) -> None: self.operate_on = operate_on self.has_conditions = has_conditions self.prior = prior self._set_active_dims(operate_on) # Since this class is a mixin, we just pass all the other parameters to the next class. super().__init__(**kwargs) # Get variables from next class: # We make it explicit here to make sure the next class really has this attributes. self._hyperparameters: list[kernels.Hyperparameter] = super().hyperparameters # type: ignore self._n_dims: int = super().n_dims # type: ignore self._len_active: int | None @property def meta(self) -> dict[str, Any]: """Returns the meta data of the created object. This method calls the `get_params` method to collect the parameters of the kernel. """ meta: dict[str, Any] = {"name": self.__class__.__name__} meta.update(self.get_params(deep=False)) # We have to handle some special cases to make the meta data serializable for k in meta: v = meta[k] if isinstance(v, AbstractKernel): meta[k] = v.meta if isinstance(v, AbstractPrior): meta[k] = v.meta if isinstance(v, np.ndarray): meta[k] = v.tolist() return meta @property def hyperparameters(self) -> list[kernels.Hyperparameter]: """Returns a list of all hyperparameter specifications.""" return self._hyperparameters @property def n_dims(self) -> int: """Returns the number of non-fixed hyperparameters of the kernel.""" return self._n_dims
[docs] def get_params(self, deep: bool = True) -> dict[str, Any]: """Get parameters of this kernel. Parameters ---------- deep : bool, defaults to True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict[str, Any] Parameter names mapped to their values. """ params = {} # ignore[misc] looks like it catches all kinds of errors, but misc is actually a category from mypy: # https://mypy.readthedocs.io/en/latest/error_code_list.html#miscellaneous-checks-misc tmp = super().get_params(deep) # type: ignore[misc] # noqa F821 args = list(tmp.keys()) # Sum and Product do not clone the 'has_conditions' attribute by default. Instead of changing their # get_params() method, we simply add the attribute here! if "has_conditions" not in args: args.append("has_conditions") for arg in args: params[arg] = getattr(self, arg, None) return params
[docs] def __call__( self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False, active: np.ndarray | None = None, ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: """Call the kernel function. Internally, `self._call` is called, which must be specified by a subclass.""" if active is None and self.has_conditions: if self.operate_on is None: active = get_conditional_hyperparameters(X, Y) else: if Y is None: active = get_conditional_hyperparameters(X[:, self.operate_on], None) else: active = get_conditional_hyperparameters(X[:, self.operate_on], Y[:, self.operate_on]) if self.operate_on is None: rval = self._call(X, Y, eval_gradient, active) else: if self._len_active is None: raise RuntimeError("The internal variable `_len_active` is not set.") if Y is None: rval = self._call( X=X[:, self.operate_on].reshape([-1, self._len_active]), Y=None, eval_gradient=eval_gradient, active=active, ) X = X[:, self.operate_on].reshape((-1, self._len_active)) else: rval = self._call( X=X[:, self.operate_on].reshape([-1, self._len_active]), Y=Y[:, self.operate_on].reshape([-1, self._len_active]), eval_gradient=eval_gradient, active=active, ) X = X[:, self.operate_on].reshape((-1, self._len_active)) Y = Y[:, self.operate_on].reshape((-1, self._len_active)) return rval
def __add__(self, b: kernels.Kernel | float) -> kernels.Sum: if not isinstance(b, kernels.Kernel): return SumKernel(self, ConstantKernel(b)) return SumKernel(self, b) def __radd__(self, b: kernels.Kernel | float) -> kernels.Sum: if not isinstance(b, kernels.Kernel): return SumKernel(ConstantKernel(b), self) return SumKernel(b, self) def __mul__(self, b: kernels.Kernel | float) -> kernels.Product: if not isinstance(b, kernels.Kernel): return ProductKernel(self, ConstantKernel(b)) return ProductKernel(self, b) def __rmul__(self, b: kernels.Kernel | float) -> kernels.Product: if not isinstance(b, kernels.Kernel): return ProductKernel(ConstantKernel(b), self) return ProductKernel(b, self) @abstractmethod def _call( self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False, active: np.ndarray | None = None, ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: """Return the kernel k(X, Y) and optionally its gradient. Note ---- Code partially copied from skopt (https://github.com/scikit-optimize). Made small changes to only compute necessary values and use scikit-learn helper functions. Parameters ---------- X : np.ndarray [#samples, #features] Left argument of the returned kernel k(X, Y). Y : np.ndarray [#samples, #features], defaults to None Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool, defaults to False Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when `Y` is None. active : np.ndarray [#samples, #features], defaults to None Boolean array specifying which hyperparameters are active. Returns ------- K : np.ndarray [#X_samples, #Y_samples] Kernel k(X, Y). K_gradient : np.ndarray [#X_samples, #X_samples, #dimensions] The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when `eval_gradient` is True. """ raise NotImplementedError def _signature(self, func: Callable) -> Signature: sig_: Signature | None try: sig_ = self._signature_cache.get(func) except AttributeError: self._signature_cache: dict[Callable, Signature] = {} sig_ = None if sig_ is None: sig = signature(func) self._signature_cache[func] = sig return sig else: return sig_ def _set_active_dims(self, operate_on: np.ndarray | None = None) -> None: """Sets dimensions this kernel should work on.""" if operate_on is not None and type(operate_on) in (list, np.ndarray): if not isinstance(operate_on, np.ndarray): raise TypeError("The argument `operate_on` needs to be of type np.ndarray but is %s" % type(operate_on)) if operate_on.dtype != int: raise ValueError("The dtype of argument `operate_on` needs to be int, but is %s" % operate_on.dtype) self.operate_on = operate_on self._len_active = len(operate_on) else: self.operate_on = None self._len_active = None
[docs]class SumKernel(AbstractKernel, kernels.Sum): """Sum kernel implementation.""" def __init__( self, k1: kernels.Kernel, k2: kernels.Kernel, operate_on: np.ndarray | None = None, has_conditions: bool = False, ) -> None: super().__init__( operate_on=operate_on, has_conditions=has_conditions, k1=k1, k2=k2, )
[docs] def __call__( self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False, active: np.ndarray | None = None, ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : np.ndarray, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y). Y : np.ndarray, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. active : np.ndarray (n_samples_X, n_features) (optional) Boolean array specifying which hyperparameters are active. Returns ------- K : np.ndarray, shape (n_samples_X, n_samples_Y) Kernel k(X, Y). K_gradient : np.ndarray (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True, active=active) K2, K2_gradient = self.k2(X, Y, eval_gradient=True, active=active) return K1 + K2, np.dstack((K1_gradient, K2_gradient)) else: return self.k1(X, Y, active=active) + self.k2(X, Y, active=active)
[docs]class ProductKernel(AbstractKernel, kernels.Product): """Product kernel implementation.""" def __init__( self, k1: kernels.Kernel, k2: kernels.Kernel, operate_on: np.ndarray | None = None, has_conditions: bool = False, ) -> None: super().__init__( operate_on=operate_on, has_conditions=has_conditions, k1=k1, k2=k2, )
[docs] def __call__( self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False, active: np.ndarray | None = None, ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : np.ndarray, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y). Y : np.ndarray, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. active : np.ndarray (n_samples_X, n_features) (optional) Boolean array specifying which hyperparameters are active. Returns ------- K : np.ndarray, shape (n_samples_X, n_samples_Y) Kernel k(X, Y). K_gradient : np.ndarray (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ if eval_gradient: K1, K1_gradient = self.k1(X, Y, eval_gradient=True, active=active) K2, K2_gradient = self.k2(X, Y, eval_gradient=True, active=active) return K1 * K2, np.dstack((K1_gradient * K2[:, :, np.newaxis], K2_gradient * K1[:, :, np.newaxis])) else: return self.k1(X, Y, active=active) * self.k2(X, Y, active=active)
[docs]class ConstantKernel(AbstractKernel, kernels.ConstantKernel): def __init__( self, constant_value: float = 1.0, constant_value_bounds: tuple[float, float] = (1e-5, 1e5), operate_on: np.ndarray | None = None, has_conditions: bool = False, prior: AbstractPrior | None = None, ) -> None: super().__init__( operate_on=operate_on, has_conditions=has_conditions, prior=prior, constant_value=constant_value, constant_value_bounds=constant_value_bounds, )
[docs] def __call__( self, X: np.ndarray, Y: np.ndarray | None = None, eval_gradient: bool = False, active: np.ndarray | None = None, ) -> np.ndarray | tuple[np.ndarray, np.ndarray]: """Return the kernel k(X, Y) and optionally its gradient. Parameters ---------- X : np.ndarray, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y). Y : np.ndarray, shape (n_samples_Y, n_features), (optional, default=None) Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead. eval_gradient : bool (optional, default=False) Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None. active : np.ndarray (n_samples_X, n_features) (optional) Boolean array specifying which hyperparameters are active. Returns ------- K : np.ndarray, shape (n_samples_X, n_samples_Y) Kernel k(X, Y). K_gradient : np.ndarray (opt.), shape (n_samples_X, n_samples_X, n_dims) The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True. """ X = np.atleast_2d(X) if Y is None: Y = X elif eval_gradient: raise ValueError("Gradient can only be evaluated when Y is None.") K = np.full( (X.shape[0], Y.shape[0]), self.constant_value, dtype=np.array(self.constant_value).dtype, ) if eval_gradient: if not self.hyperparameter_constant_value.fixed: return ( K, np.full( (X.shape[0], X.shape[0], 1), self.constant_value, dtype=np.array(self.constant_value).dtype, ), ) else: return K, np.empty((X.shape[0], X.shape[0], 0)) else: return K