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Base kernels

smac.model.gaussian_process.kernels.base_kernels #

AbstractKernel #

AbstractKernel(
    *,
    operate_on: ndarray | None = None,
    has_conditions: bool = False,
    prior: AbstractPrior | None = None,
    **kwargs: Any
)

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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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

hyperparameters property #

hyperparameters: list[Hyperparameter]

Returns a list of all hyperparameter specifications.

meta property #

meta: 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.

n_dims property #

n_dims: int

Returns the number of non-fixed hyperparameters of the kernel.

__call__ #

__call__(
    X: ndarray,
    Y: ndarray | None = None,
    eval_gradient: bool = False,
    active: ndarray | None = None,
) -> ndarray | tuple[ndarray, ndarray]

Call the kernel function. Internally, self._call is called, which must be specified by a subclass.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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

get_params #

get_params(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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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

ConstantKernel #

ConstantKernel(
    constant_value: float = 1.0,
    constant_value_bounds: tuple[float, float] = (
        1e-05,
        100000.0,
    ),
    operate_on: ndarray | None = None,
    has_conditions: bool = False,
    prior: AbstractPrior | None = None,
)

Bases: AbstractKernel, ConstantKernel

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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,
    )

hyperparameters property #

hyperparameters: list[Hyperparameter]

Returns a list of all hyperparameter specifications.

meta property #

meta: 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.

n_dims property #

n_dims: int

Returns the number of non-fixed hyperparameters of the kernel.

__call__ #

__call__(
    X: ndarray,
    Y: ndarray | None = None,
    eval_gradient: bool = False,
    active: ndarray | None = None,
) -> ndarray | tuple[ndarray, 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).

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.

bool (optional, default=False)

Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None.

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).

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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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

get_params #

get_params(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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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

ProductKernel #

ProductKernel(
    k1: Kernel,
    k2: Kernel,
    operate_on: ndarray | None = None,
    has_conditions: bool = False,
)

Bases: AbstractKernel, Product

Product kernel implementation.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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,
    )

hyperparameters property #

hyperparameters: list[Hyperparameter]

Returns a list of all hyperparameter specifications.

meta property #

meta: 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.

n_dims property #

n_dims: int

Returns the number of non-fixed hyperparameters of the kernel.

__call__ #

__call__(
    X: ndarray,
    Y: ndarray | None = None,
    eval_gradient: bool = False,
    active: ndarray | None = None,
) -> ndarray | tuple[ndarray, 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).

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.

bool (optional, default=False)

Determines whether the gradient with respect to the kernel hyperparameter is determined.

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).

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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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)

get_params #

get_params(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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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

SumKernel #

SumKernel(
    k1: Kernel,
    k2: Kernel,
    operate_on: ndarray | None = None,
    has_conditions: bool = False,
)

Bases: AbstractKernel, Sum

Sum kernel implementation.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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,
    )

hyperparameters property #

hyperparameters: list[Hyperparameter]

Returns a list of all hyperparameter specifications.

meta property #

meta: 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.

n_dims property #

n_dims: int

Returns the number of non-fixed hyperparameters of the kernel.

__call__ #

__call__(
    X: ndarray,
    Y: ndarray | None = None,
    eval_gradient: bool = False,
    active: ndarray | None = None,
) -> ndarray | tuple[ndarray, 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).

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.

bool (optional, default=False)

Determines whether the gradient with respect to the kernel hyperparameter is determined.

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).

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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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)

get_params #

get_params(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.

Source code in smac/model/gaussian_process/kernels/base_kernels.py 
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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