`sklearn.gaussian_process.kernels`.CompoundKernel#

class sklearn.gaussian_process.kernels.CompoundKernel(kernels)[source]#

Kernel which is composed of a set of other kernels.

New in version 0.18.

Parameters:

kernelslist of Kernels: The other kernels

Attributes:

bounds: Returns the log-transformed bounds on the theta.
hyperparameters: Returns a list of all hyperparameter specifications.
n_dims: Returns the number of non-fixed hyperparameters of the kernel.
requires_vector_input: Returns whether the kernel is defined on discrete structures.
theta: Returns the (flattened, log-transformed) non-fixed hyperparameters.

Examples

>>> from sklearn.gaussian_process.kernels import WhiteKernel
>>> from sklearn.gaussian_process.kernels import RBF
>>> from sklearn.gaussian_process.kernels import CompoundKernel
>>> kernel = CompoundKernel(
...     [WhiteKernel(noise_level=3.0), RBF(length_scale=2.0)])
>>> print(kernel.bounds)
[[-11.51292546  11.51292546]
 [-11.51292546  11.51292546]]
>>> print(kernel.n_dims)
2
>>> print(kernel.theta)
[1.09861229 0.69314718]

Methods

`__call__`(X[, Y, eval_gradient])	Return the kernel k(X, Y) and optionally its gradient.
`clone_with_theta`(theta)	Returns a clone of self with given hyperparameters theta.
`diag`(X)	Returns the diagonal of the kernel k(X, X).
`get_params`([deep])	Get parameters of this kernel.
`is_stationary`()	Returns whether the kernel is stationary.
`set_params`(**params)	Set the parameters of this kernel.

__call__(X, Y=None, eval_gradient=False)[source]#

Return the kernel k(X, Y) and optionally its gradient.

Note that this compound kernel returns the results of all simple kernel stacked along an additional axis.

Parameters:

Xarray-like of shape (n_samples_X, n_features) or list of object, default=None: Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_X, n_features) or list of object, default=None: Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead.
eval_gradientbool, default=False: Determines whether the gradient with respect to the log of the kernel hyperparameter is computed.

Returns:

Kndarray of shape (n_samples_X, n_samples_Y, n_kernels): Kernel k(X, Y)
K_gradientndarray of shape (n_samples_X, n_samples_X, n_dims, n_kernels), optional: The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True.

property bounds#

Returns the log-transformed bounds on the theta.

Returns:

boundsarray of shape (n_dims, 2): The log-transformed bounds on the kernel’s hyperparameters theta

clone_with_theta(theta)[source]#

Returns a clone of self with given hyperparameters theta.

Parameters:

thetandarray of shape (n_dims,): The hyperparameters

diag(X)[source]#

Returns the diagonal of the kernel k(X, X).

The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated.

Parameters:

Xarray-like of shape (n_samples_X, n_features) or list of object: Argument to the kernel.

Returns:

K_diagndarray of shape (n_samples_X, n_kernels): Diagonal of kernel k(X, X)

get_params(deep=True)[source]#

Get parameters of this kernel.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

property hyperparameters#: Returns a list of all hyperparameter specifications.

is_stationary()[source]#: Returns whether the kernel is stationary.

property n_dims#: Returns the number of non-fixed hyperparameters of the kernel.

property requires_vector_input#: Returns whether the kernel is defined on discrete structures.

set_params(**params)[source]#

Set the parameters of this kernel.

The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:

self

property theta#

Returns the (flattened, log-transformed) non-fixed hyperparameters.

Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale.

Returns:

thetandarray of shape (n_dims,): The non-fixed, log-transformed hyperparameters of the kernel

sklearn.gaussian_process.kernels.CompoundKernel#

`sklearn.gaussian_process.kernels`.CompoundKernel#