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# `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)

  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:
    gamma float, default=1.0

      Parameter gamma of the pairwise kernel specified by metric. It should be positive.

    gamma_bounds pair 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_kwargs dict, default=None

      All entries of this dict (if any) are passed as keyword arguments to the pairwise kernel function.

  Attributes:
    `bounds`

      Returns the log-transformed bounds on the theta.

    hyperparameter_gamma
    `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 fixed-length feature vectors or generic objects.

    `theta`

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

  Examples

      >>> from sklearn.datasets import load_iris
      >>> from sklearn.gaussian_process import GaussianProcessClassifier
      >>> from sklearn.gaussian_process.kernels import PairwiseKernel
      >>> X, y = load_iris(return_X_y=True)
      >>> kernel = PairwiseKernel(metric='rbf')
      >>> gpc = GaussianProcessClassifier(kernel=kernel,
      ...         random_state=0).fit(X, y)
      >>> gpc.score(X, y)
      0.9733...
      >>> gpc.predict_proba(X[:2,:])
      array([[0.8880..., 0.05663..., 0.05532...],
             [0.8676..., 0.07073..., 0.06165...]])

  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)

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

    Parameters:
      X ndarray of shape (n_samples_X, n_features)

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

      Y ndarray 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_gradient bool, default=False

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

    Returns:
      K ndarray of shape (n_samples_X, n_samples_Y)

        Kernel k(X, Y)

      K_gradient ndarray of shape (n_samples_X, n_samples_X, n_dims), 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:
      bounds ndarray of shape (n_dims, 2)

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

  clone_with_theta(theta)

    Returns a clone of self with given hyperparameters theta.

    Parameters:
      theta ndarray of shape (n_dims,)

        The hyperparameters

  diag(X)

    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:
      X ndarray of shape (n_samples_X, n_features)

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

    Returns:
      K_diag ndarray of shape (n_samples_X,)

        Diagonal of kernel k(X, X)

  get_params(deep=True)

    Get parameters of this kernel.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  property hyperparameters

    Returns a list of all hyperparameter specifications.

  is_stationary()

    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 fixed-length feature vectors or generic objects. Defaults to True for backward compatibility.

  set_params(**params)

    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:
      theta ndarray of shape (n_dims,)

        The non-fixed, log-transformed hyperparameters of the kernel