sklearn.gaussian_process.kernels.PairwiseKernel¶
-
class
sklearn.gaussian_process.kernels.PairwiseKernel(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear', pairwise_kernels_kwargs=None)[source]¶ Wrapper for kernels in sklearn.metrics.pairwise.
A thin wrapper around the functionality of the kernels in sklearn.metrics.pairwise.
- Note: Evaluation of eval_gradient is not analytic but numeric and all
kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel parameters are set directly at initialization and are kept fixed.
New in version 0.18.
- Parameters
- gammafloat, default=1.0
Parameter gamma of the pairwise kernel specified by metric. It should be positive.
- gamma_boundspair of floats >= 0 or “fixed”, default=(1e-5, 1e5)
The lower and upper bound on ‘gamma’. If set to “fixed”, ‘gamma’ cannot be changed during hyperparameter tuning.
- metric{“linear”, “additive_chi2”, “chi2”, “poly”, “polynomial”, “rbf”, “laplacian”, “sigmoid”, “cosine”} or callable, default=”linear”
The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.
- pairwise_kernels_kwargsdict, default=None
All entries of this dict (if any) are passed as keyword arguments to the pairwise kernel function.
- Attributes
boundsReturns the log-transformed bounds on the theta.
- hyperparameter_gamma
hyperparametersReturns a list of all hyperparameter specifications.
n_dimsReturns the number of non-fixed hyperparameters of the kernel.
requires_vector_inputReturns whether the kernel is defined on fixed-length feature vectors or generic objects.
thetaReturns the (flattened, log-transformed) non-fixed hyperparameters.
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.
Returns whether the kernel is stationary.
set_params(**params)Set the parameters of this kernel.
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__init__(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear', pairwise_kernels_kwargs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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__call__(X, Y=None, eval_gradient=False)[source]¶ Return the kernel k(X, Y) and optionally its gradient.
- Parameters
- Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
- Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead.
- eval_gradientbool, default=False
Determines whether the gradient with respect to the kernel hyperparameter is determined. Only supported when Y is None.
- 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_gradientis True.
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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
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clone_with_theta(theta)[source]¶ Returns a clone of self with given hyperparameters theta.
- Parameters
- thetandarray of shape (n_dims,)
The hyperparameters
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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
- Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
- Returns
- K_diagndarray of shape (n_samples_X,)
Diagonal of kernel k(X, X)
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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.
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property
hyperparameters¶ Returns a list of all hyperparameter specifications.
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property
n_dims¶ Returns the number of non-fixed hyperparameters of the kernel.
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property
requires_vector_input¶ Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility.
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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
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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