sklearn.gaussian_process.kernels.Product

class sklearn.gaussian_process.kernels.Product(k1, k2)[source]

The Product kernel takes two kernels \(k_1\) and \(k_2\) and combines them via

\[k_{prod}(X, Y) = k_1(X, Y) * k_2(X, Y)\]

Note that the __mul__ magic method is overridden, so Product(RBF(), RBF()) is equivalent to using the * operator with RBF() * RBF().

Read more in the User Guide.

New in version 0.18.

Parameters
k1Kernel

The first base-kernel of the product-kernel

k2Kernel

The second base-kernel of the product-kernel

Attributes
bounds

Returns the log-transformed bounds on the theta.

hyperparameters

Returns a list of all hyperparameter.

n_dims

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

requires_vector_input

Returns whether the kernel is stationary.

theta

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

Examples

>>> from sklearn.datasets import make_friedman2
>>> from sklearn.gaussian_process import GaussianProcessRegressor
>>> from sklearn.gaussian_process.kernels import (RBF, Product,
...            ConstantKernel)
>>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
>>> kernel = Product(ConstantKernel(2), RBF())
>>> gpr = GaussianProcessRegressor(kernel=kernel,
...         random_state=0).fit(X, y)
>>> gpr.score(X, y)
1.0
>>> kernel
1.41**2 * RBF(length_scale=1)

Methods

__call__(self, X[, Y, eval_gradient])

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

clone_with_theta(self, theta)

Returns a clone of self with given hyperparameters theta.

diag(self, X)

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

get_params(self[, deep])

Get parameters of this kernel.

is_stationary(self)

Returns whether the kernel is stationary.

set_params(self, \*\*params)

Set the parameters of this kernel.

__init__(self, k1, k2)[source]

Initialize self. See help(type(self)) for accurate signature.

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

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

Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object

Left argument of the returned kernel k(X, Y)

Yarray-like of shape (n_samples_Y, 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 kernel hyperparameter is determined.

Returns
Kndarray of shape (n_samples_X, n_samples_Y)

Kernel k(X, Y)

K_gradientndarray of shape (n_samples_X, n_samples_X, n_dims), optional

The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True.

property bounds

Returns the log-transformed bounds on the theta.

Returns
boundsndarray of shape (n_dims, 2)

The log-transformed bounds on the kernel’s hyperparameters theta

clone_with_theta(self, theta)[source]

Returns a clone of self with given hyperparameters theta.

Parameters
thetandarray of shape (n_dims,)

The hyperparameters

diag(self, 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,)

Diagonal of kernel k(X, X)

get_params(self, 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.

is_stationary(self)[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 stationary.

set_params(self, **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