graphdot.kernel.molecular module

class graphdot.kernel.molecular.Tang2019MolecularKernel(stopping_probability=0.01, starting_probability=1.0, element_prior=0.2, edge_length_scale=0.05, **kwargs)

Bases: object

A margianlized graph kernel for 3D molecular structures as in: Tang, Y. H., & de Jong, W. A. (2019). Prediction of atomization energy using graph kernel and active learning. The Journal of chemical physics, 150(4), 044107. The kernel can be directly used together with Graph.from_ase() to operate on molecular structures.

Parameters:

• stopping_probability (float in (0, 1)) – The probability for the random walk to stop during each step.
• starting_probability (float) – The probability for the random walk to start from any node. See the p kwarg of graphdot.kernel.marginalized.MarginalizedGraphKernel
• element_prior (float in (0, 1)) – The baseline similarity between distinct elements — an element always have a similarity 1 to itself.
• edge_length_scale (float in (0, inf)) – length scale of the Gaussian kernel on edge length. A rule of thumb is that the similarity decays smoothly from 1 to nearly 0 around three times of the length scale.

__call__(X, Y=None, **kwargs)

Same call signature as graphdot.kernel.marginalized.MarginalizedGraphKernel.__call__()

bounds

clone_with_theta(theta)

diag(X, **kwargs)

Same call signature as graphdot.kernel.marginalized.MarginalizedGraphKernel.diag()

hyperparameter_bounds

hyperparameters

theta