Support for random walk kernel #29
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| # Author: Henry Moss & Ryan-Rhys Griffiths | |||
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Haha was waiting for permission
| # Author: Henry Moss & Ryan-Rhys Griffiths | ||
| """ | ||
| Molecule kernels for Gaussian Process Regression implemented in GPflow. | ||
| """ |
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Shall we change this module-level docstring?
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| # Author: Henry Moss & Ryan-Rhys Griffiths | |||
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Having a kernel_modules directory is probably a good idea.
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I'm happy for it to have another (less clumsy) name but yeah I think it was the only way for me to implement the kernel without contaminating pre-existing code
tests/kernels/test_random_walk.py
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| from GP.kernel_modules.random_walk import RandomWalk | ||
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| sys.path.append('/Users/juliusschwartz/Mystuff/FlowMO') |
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I hope so xD This was absolutely a hack and I completely forgot that I'd left a hardcoded file path in here. I'd definitely get this PR rejected at work
| from .kernel_utils import normalize | ||
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| class RandomWalk(gpflow.kernels.Kernel): |
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Should we have some documentation for the class?
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Definitely. I suppose I hoped for the main logic to be looked at first but honestly I should have made a separate feature branch and done multiple PRs onto that (one for the logic, one for documentation etc)
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I'll just add Leo to the review since he coded up the random walk kernel in GProTorch. |
Good idea (I don't seem to be able to add people myself) |
| :param X: array of N graph objects (represented as adjacency matrices of varying sizes). | ||
| :return: N x 1 array. | ||
| """ | ||
| return tf.linalg.tensor_diag_part(self.K(X)) |
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Couldn't think of anything smarter than implementing this function like this
Converting the work done in https://github.com/leojklarner/GProTorch/blob/kernels/gprotorch/kernels/graph_kernels/random_walk.py
from pytorch to tensorflow and using https://www.jmlr.org/papers/volume11/vishwanathan10a/vishwanathan10a.pdf as a reference.
Have only implented eigendecomposition approach so far (meaning that one test for which GraKel uses Conjugate Gradient Descent only passes if
method_type="baseline"is specified as an argument to the constructor of GraKel's random walk kernel)Tests with non-null values of
pdon't pass although I suspect this might be due to a bug in GraKel (see ysig/GraKeL#71)