himalaya.kernel_ridge.solve_multiple_kernel_ridge_random_search

himalaya.kernel_ridge.solve_multiple_kernel_ridge_random_search(Ks, Y, n_iter=100, concentration=[0.1, 1.0], alphas=1.0, score_func=<function l2_neg_loss>, cv=5, fit_intercept=False, return_weights=None, Xs=None, local_alpha=True, jitter_alphas=False, random_state=None, n_targets_batch=None, n_targets_batch_refit=None, n_alphas_batch=None, progress_bar=True, Ks_in_cpu=False, conservative=False, Y_in_cpu=False, diagonalize_method='eigh', return_alphas=False)

Solve multiple kernel ridge regression using random search.

Parameters

Ksarray of shape (n_kernels, n_samples, n_samples)

Input kernels.

Yarray of shape (n_samples, n_targets)

Target data.

n_iterint, or array of shape (n_iter, n_kernels)

Number of kernel weights combination to search. If an array is given, the solver uses it as the list of kernel weights to try, instead of sampling from a Dirichlet distribution. Examples:

• n_iter=np.eye(n_kernels) implement a winner-take-all strategy over kernels.

• n_iter=np.ones((1, n_kernels))/n_kernels solves a (standard) kernel ridge regression.

concentrationfloat, or list of float

Concentration parameters of the Dirichlet distribution. If a list, iteratively cycle through the list. Not used if n_iter is an array.

alphasfloat or array of shape (n_alphas, )

Range of ridge regularization parameter.

score_funccallable

Function used to compute the score of predictions versus Y.

cvint or scikit-learn splitter

Cross-validation splitter. If an int, KFold is used.

fit_interceptboolean

Whether to fit an intercept. If False, Ks should be centered (see KernelCenterer), and Y must be zero-mean over samples. Only available if return_weights == ‘dual’.

return_weightsNone, ‘primal’, or ‘dual’

Whether to refit on the entire dataset and return the weights.

Xsarray of shape (n_kernels, n_samples, n_features) or None

Necessary if return_weights == ‘primal’.

local_alphabool

If True, alphas are selected per target, else shared over all targets.

jitter_alphasbool

If True, alphas range is slightly jittered for each gamma.

random_stateint, or None

Random generator seed. Use an int for deterministic search.

n_targets_batchint or None

Size of the batch for over targets during cross-validation. Used for memory reasons. If None, uses all n_targets at once.

n_targets_batch_refitint or None

Size of the batch for over targets during refit. Used for memory reasons. If None, uses all n_targets at once.

n_alphas_batchint or None

Size of the batch for over alphas. Used for memory reasons. If None, uses all n_alphas at once.

progress_barbool

If True, display a progress bar over gammas.

Ks_in_cpubool

If True, keep Ks in CPU memory to limit GPU memory (slower). This feature is not available through the scikit-learn API.

conservativebool

If True, when selecting the hyperparameter alpha, take the largest one that is less than one standard deviation away from the best. If False, take the best.

Y_in_cpubool

If True, keep the target values Y in CPU memory (slower).

diagonalize_methodstr in {“eigh”, “svd”}

Method used to diagonalize the kernel.

return_alphasbool

If True, return the best alpha value for each target.

Returns

deltasarray of shape (n_kernels, n_targets)

Best log kernel weights for each target.

refit_weightsarray or None

Refit regression weights on the entire dataset, using selected best hyperparameters. Refit weights are always stored on CPU memory. If return_weights == ‘primal’, shape is (n_features, n_targets), if return_weights == ‘dual’, shape is (n_samples, n_targets), else, None.

cv_scoresarray of shape (n_iter, n_targets)

Cross-validation scores per iteration, averaged over splits, for the best alpha. Cross-validation scores will always be on CPU memory.

best_alphasarray of shape (n_targets, )

Best alpha value per target. Only returned if return_alphas is True.

interceptarray of shape (n_targets,)

Intercept. Only returned when fit_intercept is True.