Module risk¶
operalib.risk
implements risk model and their gradients.
-
class
operalib.risk.
KernelRidgeRisk
(lbda)¶ Define Kernel ridge risk and its gradient.
Methods
__call__
(dual_coefs, ground_truth, Gram)Compute the Empirical OVK ridge risk. functional_grad
(dual_coefs, ground_truth, Gram)Compute the gradient of the Empirical OVK ridge risk. functional_grad_val
(dual_coefs, ...)Compute the gradient and value of the Empirical OVK ridge risk. -
__call__
(dual_coefs, ground_truth, Gram)¶ Compute the Empirical OVK ridge risk.
Parameters: dual_coefs : {vector-like}, shape = [n_samples1 * n_targets]
Coefficient to optimise
ground_truth : {vector-like}
Targets samples
Gram : {LinearOperator}
Gram matrix acting on the dual_coefs
Returns: float : Empirical OVK ridge risk
-
__init__
(lbda)¶ Initialize Empirical kernel ridge risk.
Parameters: lbda : {float}
Small positive values of lbda improve the conditioning of the problem and reduce the variance of the estimates. Lbda corresponds to
(2*C)^-1
in other linear models such as LogisticRegression or LinearSVC.
-
__weakref__
¶ list of weak references to the object (if defined)
-
functional_grad
(dual_coefs, ground_truth, Gram)¶ Compute the gradient of the Empirical OVK ridge risk.
Parameters: dual_coefs : {vector-like}, shape = [n_samples1 * n_targets]
Coefficient to optimise
ground_truth : {vector-like}
Targets samples
Gram : {LinearOperator}
Gram matrix acting on the dual_coefs
Returns: {vector-like} : gradient of the Empirical OVK ridge risk
-
functional_grad_val
(dual_coefs, ground_truth, Gram)¶ Compute the gradient and value of the Empirical OVK ridge risk.
Parameters: dual_coefs : {vector-like}, shape = [n_samples1 * n_targets]
Coefficient to optimise
ground_truth : {vector-like}
Targets samples
Gram : {LinearOperator}
Gram matrix acting on the dual_coefs
Returns: Tuple{float, vector-like} : Empirical OVK ridge risk and its gradient
returned as a tuple.
-