Combine kernels hierarchy

neps.optimizers.bayesian_optimization.kernels.combine_kernels_hierarchy

SumKernel

SumKernel(*kernels, **kwargs)

Bases: CombineKernel

Source code in neps/optimizers/bayesian_optimization/kernels/combine_kernels_hierarchy.py

def __init__(self, *kernels, **kwargs):
    super().__init__("sum", *kernels, **kwargs)

forward_t

forward_t(
    weights: Tensor,
    gr2: list,
    x2=None,
    gr1: list = None,
    x1=None,
    feature_lengthscale=None,
)

Compute the kernel gradient w.r.t the feature vector Parameters

---

feature_lengthscale x2 x1 gr1 weights gr2

Returns

grads: k list of 2-tuple. (K, x2) where K is the weighted Gram matrix of that matrix, x2 is the leaf variable on which Jacobian-vector product to be computed.

Source code in neps/optimizers/bayesian_optimization/kernels/combine_kernels_hierarchy.py

def forward_t(
    self,
    weights: torch.Tensor,
    gr2: list,
    x2=None,
    gr1: list = None,
    x1=None,
    feature_lengthscale=None,
):
    """
    Compute the kernel gradient w.r.t the feature vector
    Parameters
    ----------
    feature_lengthscale
    x2
    x1
    gr1
    weights
    gr2

    Returns
    -------
    grads: k list of 2-tuple.
    (K, x2) where K is the weighted Gram matrix of that matrix, x2 is the leaf variable on which Jacobian-vector
    product to be computed.

    """
    weights = transform_weights(weights.clone())
    grads = []
    for i, k in enumerate(self.kernels):
        if isinstance(k, GraphKernels):
            handle = k.forward_t(gr2, gr1=gr1)
            grads.append((weights[i] * handle[0], handle[1], handle[2]))
        elif isinstance(k, Stationary):
            handle = k.forward_t(x2=x2, x1=x1, l=feature_lengthscale)
            grads.append((weights[i] * handle[0], handle[1], handle[2]))
        else:
            logging.warning(
                "Gradient not implemented for kernel type" + str(k.__name__)
            )
            grads.append((None, None))
    assert len(grads) == len(self.kernels)
    return grads